Artykuły w czasopismach: „Sentient Machine”

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Bronfman, Zohar, Simona Ginsburg i Eva Jablonka. "When Will Robots Be Sentient?" Journal of Artificial Intelligence and Consciousness 08, nr 02 (6.08.2021): 183–203. http://dx.doi.org/10.1142/s2705078521500168.

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The current failure to construct an artificial intelligence (AI) agent with the capacity for domain-general learning is a major stumbling block in the attempt to build conscious robots. Taking an evolutionary approach, we previously suggested that the emergence of consciousness was entailed by the evolution of an open-ended domain-general form of learning, which we call unlimited associative learning (UAL). Here, we outline the UAL theory and discuss the constraints and affordances that seem necessary for constructing an AI machine exhibiting UAL. We argue that a machine that is capable of domain-general learning requires the dynamics of a UAL architecture and that a UAL architecture requires, in turn, that the machine is highly sensitive to the environment and has an ultimate value (like self-persistence) that provides shared context to all its behaviors and learning outputs. The implementation of UAL in a machine may require that it is made of “soft” materials, which are sensitive to a large range of environmental conditions, and that it undergoes sequential morphological and behavioral co-development. We suggest that the implementation of these requirements in a human-made robot will lead to its ability to perform domain-general learning and will bring us closer to the construction of a sentient machine.

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Castro, Paulo. "Lying, computers and self-awareness". Kairos. Journal of Philosophy & Science 24, nr 1 (1.12.2020): 10–34. http://dx.doi.org/10.2478/kjps-2020-0009.

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Abstract From the initial analysis of John Morris in 1976 about if computers can lie, I have presented my own treatment of the problem using what can be called a computational lying procedure. One that uses two Turing Machines. From there, I have argued that such a procedure cannot be implemented in a Turing Machine alone. A fundamental difficulty arises, concerning the computational representation of the self-knowledge a machine should have about the fact that it is lying. Contrary to Morris’ claim, I have thus suggested that computers – as far as they are Turing Machines – cannot lie. Consequently, I have claimed that moral agency attribution to a robot or any other automated AI system, cannot be made, strictly grounded on imitating behaviors. Self-awareness as an ontological grounding for moral attribution must be evoked. This can pose a recognition problem from our part, should the sentient system be the only agent capable of acknowledging its own sentience.

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Tellols, Dolça, Maite Lopez-Sanchez, Inmaculada Rodríguez, Pablo Almajano i Anna Puig. "Enhancing sentient embodied conversational agents with machine learning". Pattern Recognition Letters 129 (styczeń 2020): 317–23. http://dx.doi.org/10.1016/j.patrec.2019.11.035.

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Biever, Celeste. "9 Will we ever build a sentient machine?" New Scientist 206, nr 2754 (marzec 2010): 32–33. http://dx.doi.org/10.1016/s0262-4079(10)60801-9.

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Torrens, Paul M. "Smart and Sentient Retail High Streets". Smart Cities 5, nr 4 (29.11.2022): 1670–720. http://dx.doi.org/10.3390/smartcities5040085.

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Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.

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Smith, G. W. "Art, Aliens and the Machine". Leonardo 51, nr 5 (październik 2018): 551–52. http://dx.doi.org/10.1162/leon_a_01222.

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With the assumption that the extraordinary discoveries being made in planetary science will soon trigger a conviction of the likelihood of contact with sentient extraterrestrial beings—and with the further realization that such beings may well depend upon our visual arts as their primary point of reference in respect to our own species—this short paper uses an imaginative approach to develop some corollary ideas, and, in addition, to throw a spotlight on pioneering “systems art” theorist and visionary cosmic citizen Jack Burnham.

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Smith, Dale. "The Trouble with Sentient Beings, and: The Time Machine Paradox". Colorado Review 37, nr 3 (2010): 144–46. http://dx.doi.org/10.1353/col.2010.0037.

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Jocz, Artur. "If Machines Want to Dream... Adam Wiśniewski-Snerg on Ethical Consequences of There Being No Substantial Distinction between Humans and Robots". ETHICS IN PROGRESS 10, nr 2 (10.10.2019): 45–51. http://dx.doi.org/10.14746/eip.2019.2.5.

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Adam Wiśniewski-Snerg (1937-1995) was a Polish science fiction writer. In his novel Robot (1973), he made an attempt at a literary visualization of a machine acquiring human identity. In this article I would like to follow the ethical consequences of such situations in created literary worlds. It is worth remembering, however, that these artistic worlds often serve to test non-literary reality. In his novel, Wiśniewski-Snerg also dealt with the problem of human feelings (e.g. moral dilemmas) in a thinking machine, which is formed in the image and likeness of a human being. Such literary reflection is valuable, partly because it enters into an interesting dialogue with the work of Bruno Schulz (1892- 1942), one of the most important Polish writers of the 20th century. It is also one of the first attempts in Polish literature to address the issue of sentient machines, and is a kind of preview of contemporary dilemmas connected with the work on the creation of artificial intelligence. An example of such a dilemma is the issue of the sentient machine’s perception of the tasks imposed on it by the human-constructor. Perhaps it will start to experience them as a kind of unethical oppression. In Wiśniewski-Snerg’s writing this problem of is, of course, expressed in a metaphorical way.

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PATRA, INDRAJIT. "The Battle Within and the Battle Without: The Posthuman Worldview of Ken MacLeod’s The Corporation Wars Trilogy". American, British and Canadian Studies 37, nr 1 (1.12.2021): 85–104. http://dx.doi.org/10.2478/abcsj-2021-0019.

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Abstract The present essay seeks to analyze Scottish science fiction writer Ken MacLeod’s The Corporation Wars trilogy (2016-2017) as an amalgam of politico-philosophical ideas set against the background of posthumanism. MacLeod’s far-future posthuman world-building relies on the conventional tropes of science fiction (man-machine hybrids, brain uploading, digital resurrection, and the agency of sentient machines) to engage with pressing ideologies (the master-slave dialectics, the historical perpetuation of age-old conflict between progressive and reactionary forces, the ethics of machinic consciousness). MacLeod’s novels project a postbinarist worldview where outmoded binary oppositions between life and death, the real and the virtual, the human and the machinic are constantly abolished, but which still preserves persistent ideological divisions.

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Wilson, Daniel C. S., Mariona Coll Ardanuy, Kaspar Beelen, Barbara McGillivray i Ruth Ahnert. "The Living Machine: A Computational Approach to the Nineteenth-Century Language of Technology". Technology and Culture 64, nr 3 (lipiec 2023): 875–902. http://dx.doi.org/10.1353/tech.2023.a903976.

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abstract: This article examines a long-standing question in the history of technology concerning the trope of the living machine. The authors do this by using a cutting-edge computational method, which they apply to large collections of digitized texts. In particular, they demonstrate the affordances of a neural language model for historical research. In a deliberate maneuver, the authors use a type of model, often portrayed as sentient today, to detect figures of speech in nineteenth-century texts that portrayed machines as self-acting, automatic, or alive. Their masked language model detects unusual or surprising turns of phrase, which could not be discovered using simple keyword search. The authors collect and close read such sentences to explore how figurative language produced a context that conceived humans and machines as interchangeable in complicated ways. They conclude that, used judiciously, language models have the potential to open up new avenues of historical research.

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Hong, Eun-sook. "Neo-Colonialism in The Age of Artificial Intelligence: Sentient Machine and Singularity in Transcendence". Journal of the Humanities 89 (31.12.2019): 254–84. http://dx.doi.org/10.21211/jhum.89.8.

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Ginabila, Ginabila, i Ahmad Fauzi. "Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine". Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 6, nr 2 (20.07.2023): 111–22. http://dx.doi.org/10.47324/ilkominfo.v6i2.180.

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Abstrak: Manusia memiliki kebutuhan preferensi musik yang yang sangat beragam, oleh karena itu pemutar musik online menjadi salah satu solusi untuk memenuhi kebutuhan ini dengan menyediakan katalog musik yang luas. Analisis sentimen adalah proses untuk mengevaluasi dan mengklasifikasikan sentimen atau perasaan di balik teks atau data yang diberikan. Dalam konteks ini, analisis sentimen dilakukan pada pemutar musik online Spotify. Dua algoritma yang umum digunakan untuk analisis sentimen adalah Naive Bayes dan Support Vector Machine (SVM). Kedua algoritma ini dapat diterapkan dalam analisis sentimen pada pemutar musik online. Data teks seperti ulasan atau komentar pengguna dikumpulkan dan dilabeli dengan sentimen yang sesuai. Hasil dari penelitian menggunakan kedua algoritma ini menghasilkan nilai akurasi yang hampir sama baiknya. Algoritma Support Vector Machine menghasilkan tingkat akurasi sebesar 82,42%, sedangkan untuk Algoritma Naive Bayes mencapai 84,73%.Kata kunci: Analisis Sentimen, Naive Bayes, Support Vector MachineAbstract: Humans have diverse music preferences and online music players are a solution to meet these needs by providing a wide music catalog. Sentiment analysis is the process of evaluating and classifying sentiments or feelings behind given texts or data. In this context, sentiment analysis is performed on Spotify online music players. Two common algorithms used for sentiment analysis are Naive Bayes and Support Vector Machine (SVM). Both algorithms can be applied in sentiment analysis for online music players. Text data such as user reviews or comments are collected and labeled with corresponding sentiments. The results of the research using both algorithms yielded similar high accuracy. The Support Vector Machine algorithm achieved an accuracy rate of 82.42%, while the Naive Bayes algorithm reached 84.73%.Keywords: Sentiment Analysis, Naive Bayes, Support Vector Machine

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Wennerscheid, Sophie. "Things don’t cry, do they? Emotional attachment between humans, technology and nature in Harry Martinson’s epic space poem Aniara and the science fiction film Aniara". Journal of Scandinavian Cinema 11, nr 1 (1.03.2021): 31–48. http://dx.doi.org/10.1386/jsca_00036_1.

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While Harry Martinson’s epic space poem Aniara (1956) has received little attention outside Sweden over the last half-century, several new adaptations have appeared in recent years, most notably the 2018 science fiction film Aniara. This article explores the reason for this renewed interest and argues that, in addition to ecocritical aspects, it is the interest in human‐machine relations that has contributed to the rediscovery. Drawing on Jane Bennett’s notion of thing-power, the article focuses on the spaceship Aniara’s artificial intelligence, Mima. Both in Martinson’s text and the film adaptation, Mima is depicted as a sentient machine that does not show empathy with suffering humans but rather with the suffering of nature, epitomized in crying stones. Analysing the motif of the crying stones in more detail, the article seeks to contribute to the discussion about emotional attachment between humans, technology and nature.

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Rana, Muhammad Rizwan Rashid, Asif Nawaz i Javed Iqbal. "A survey on sentiment classification algorithms, challenges and applications". Acta Universitatis Sapientiae, Informatica 10, nr 1 (1.08.2018): 58–72. http://dx.doi.org/10.2478/ausi-2018-0004.

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Abstract Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects

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Galíndez Pérez, Jorge Luís. "Uso de la Inteligencia Artificial y el Metaverso: Optimización de estrategias para la aplicación de la Nuevas Tecnologías en diversas áreas del conocimiento". Revista Latinoamericana de Difusión Científica 6, nr 10 (6.01.2024): 316–28. http://dx.doi.org/10.38186/difcie.610.18.

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La convergencia de la Inteligencia Artificial (IA) y el Metaverso (Meta) abre oportunidades sin precedentes para la educación y el aprendizaje. Sin embargo, esta integración plantea importantes desafíos éticos, culturales y pedagógicos. Este artículo analiza tres perspectivas clave sobre el uso de la Inteligencia Artificial y el Metaverso en la educación, basadas en: "The Sentient Machine" escrito por Amir Husain; "Experience on Demand" desarrollado por Jeremy Bailenson; y "Teaching Machines" generado por Audrey Watters. Tomando como punto de partida la comparación y el contraste de estas tres visiones, se consideran las cuestiones involucradas de la manera más lateral posible, abordando las dimensiones ontológicas, axiológicas, etnográficas y epistemológicas de esta convergencia tecnológica. Se argumenta que si bien la IA y el Metaverso ofrecen un enorme potencial para la optimización de estrategias educativas, se requiere un enfoque holístico e inclusivo para materializar estos beneficios y evitar una profundización de la brecha digital. El concepto de "Salto Digital" se propone como una hoja de ruta para garantizar un desarrollo ético, equitativo y socialmente responsable de la educación mediada por IA y el Metaverso.

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La Russa, F. M., i C. Santagati. "HISTORICAL SENTIENT – BUILDING INFORMATION MODEL: A DIGITAL TWIN FOR THE MANAGEMENT OF MUSEUM COLLECTIONS IN HISTORICAL ARCHITECTURES". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (25.08.2020): 755–62. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-755-2020.

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Abstract. This paper investigates the application of the Digital Twin approach to get a Sentient building able to acquire the ability to perceive external inputs and develop strategies to support its management and/or conservation. The experimentation foresees the integration of an H-BIM model with a Decision Support System based on Artificial Intelligence (in this case Machine Learning techniques) for the management of museum collections in historical architectures. The innovative aspect of this methodology resides in the change of paradigm regarding the relations between the historical building under consideration and the professional figures who deal with the management, conservation and architectural restoration. This work tries to contextualize the novel HS-BIM methodology within the theoretical discussion of the disciplines mentioned above and to participate in Digital Twin’s debate. HS-BIM can be seen as a possible path that leads to creating digital twins for cultural heritage. The reflection inspired by this experience aims to revise the concept of Digital Twin as a parallel/external digital model in favour of an artificial evolution of the real system augmented by a “cognitive” apparatus. In this vision, thanks to AI application, future buildings will be able to sense “comfort and pain” and learning from their own life-cycle experience but also from that one of elder sentient-buildings thanks to transfer learning already applied in AI’s fields.

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V, Ramesh Kumar, i Loganathan Muthusamy. "Autonomous Droid for Terrestrial Research and Intelligence (ATRi)". Acceleron Aerospace Journal 2, nr 1 (30.01.2024): 148–49. http://dx.doi.org/10.61359/11.2106-2404.

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ATRi (Autonomous droid for Terrestrial Research and Intelligence) is an autonomous and sentient droid designed to assist space crews with various activities and experiments. The artificially intelligent droid autonomously navigates inside crewed space capsules and utilizes voice and visual recognition algorithms to receive commands, record basic readings, and assist the crew in performing various experiments. ATRi's machine learning algorithms can be tailored to the specific astronaut(s) it will accompany in the space capsule. This personalized approach not only assists astronauts but also fosters a sense of companionship. This paper provides details about the physical and algorithmic characteristics of the droid and outlines how it can be trained and deployed in any crewed space capsule. The droid will be powered by a sentient program that incorporates visual recognition (including facial recognition and video recording capabilities), natural language processing, voice recognition, and speech synthesis. Six microphones and two cameras are embedded to capture audio/voice commands and visuals. While the current version of the droid is fixed to the space capsule, future iterations are envisioned to be highly mobile in zero-gravity environments within any crewed space capsule. This mobility is facilitated by a sophisticated motion control system that enables the droid to align in any direction, rotate, and navigate inside the crewed space module. In addition to assisting the crew, ATRi will document all activities inside the capsule through photos and videos. It can process images and videos, automatically categorize them, and periodically communicate the information to ground control.

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Narasimham, Ayalasomayajula Appala. "SENTIMENTAL ANALYSIS ON TOURISM REVIEWS". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 04 (29.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem32368.

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Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

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Fadliansyah, Muhammad, Setio Basuki i Yufis Azhar. "Prediksi Harga Saham Menggunakan Sentimen Pilkada DKI Jakarta 2017 Dengan Algoritma Support Vector Machine". Jurnal Repositor 2, nr 12 (4.12.2020): 1623. http://dx.doi.org/10.22219/repositor.v2i12.444.

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AbstrakTwitter merupakan salah satu sosial media yang paling banyak dipakai di Indonesia, tidak hanya sebagai sarana berbagi informasi terkait hal – hal pribadi tetapi juga bisa berupa opini terhadap suatu topik. Tidak hanya sebagai pusat infromasi, twitter juga bisa digunakan sebagai pusat data berupa teks. Pilkada DKI Jakarta 2017 merupakan salah satu topik yang menarik untuk di bahas. Tidak hanya sebagai penentu kepemimpinan Jakarta untuk 5 tahun kedepan, tetapi karena pengaruh yang dimilikinya terhadap beberapa sektor di Indoensia. Tweet yang membahas topik Pilkada DKI Jakarta 2017 bisa diolah untuk mendapatkan informasi yang berguna, misalnya sentimen yang terjadi selama peristiwa politik ini terjadi. Sentimen yang didapat bisa digunakan dalam prediksi harga saham selama masa Pilkada. Untuk bisa mendapatkan sentimen dari data teks dari twitter, sentiment anaylsis digunakan untuk mengekstrak informasi dari tweet yang sudah dikumpulkan. Untuk melakukan sentiment analysis, algoritma support vector machine dipakai untuk mengklasifikasikan tweet kedalam target kelas. Hasil dari klasifikasi sentimen digunakan sebagai salah satu pembobot dalam regresi linier untuk memprediksi harga saham. Hasil dari pengujian menunjukkan bahwa penggunaan sentimen Pilkada DKI Jakarta 2017 untuk memprediksi harga saham cukup baik. Dimana nilai RMSE yang didapat oleh masing-masing saham bervariasi karena saham-saham yang dipilih berasal dari sektor yang berbeda. BBRI 58.974, SRTG 101.188, WIKA 52.042, ADHI 93.420 dan APLN 17.342.Abstract Twitter is one of the most widely used social media in Indonesia, not only as a means of sharing information related to personal matters but also as information. Not only as a center of information, twitter can also be as central data in the form of text. DKI Jakarta Election 2017 is one of the interesting topics to discuss. Not only as a determinant of Jakarta's leadership for the next 5 years, but because of the influence it has had on several sectors in Indonesia. A Tweet that discusses the topic of the 2017 DKI Jakarta Regional Election can be processed to get useful information, for example sentiments that occur during times. Sentiment that can be done in the context of prices during the election period. To be able to get sentiments from text data from twitter, anaylsis sentiment is to extract information from tweets that have been collected. To do sentiment analysis, the support vector machine algorithm is used to classify tweets in the target class. Results from the basis of sentiment as one weight in linear regression to predict prices. The results of the test show that the use of the DKI Jakarta Regional Election sentiment 2017 is to predict the stock price to be quite good. Where is the RMSE value that can be found by each different sector. BBRI 58,974, SRTG 101,188, WIKA 52,042, ADHI 93,420 and APLN 17,342.

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Yang, Shu. "What is Life? The Artistic Aspect of the Embodied Intelligence and Soft Robotics: Life in Sonic Arts, Audio-Visual Programming, and Handicrafts Design". IOP Conference Series: Materials Science and Engineering 1292, nr 1 (1.10.2023): 012016. http://dx.doi.org/10.1088/1757-899x/1292/1/012016.

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Abstract The artistic aspect is the ‘dancer’ (Gary Zukav) [7] in the machine. There is an embryonic potentiality for embodied intelligence, soft robotics, and engineering if we emphasise the artistic aspect and employ it as an indispensable element. Art is less weighted in science, technology, and engineering than functionalities and applications. Equivalently, there is a potential under-explored advantage of science and technology-based artistic creation instead of individualistic improvisation, interpretation, and expression. Starting from the philosophical and scientific question, What is life? this paper reviewed influential theories from entropy, ‘code-script’ by Erwin Schrödinger, ‘wetware’, cell compartmentation by Paul Nurse, sentient machine by Anil Seth, to the ‘Dance’ by Gary Zukav, the ‘30 000’ organ by Denis Noble, the 100,000 protein sonification by Markus Buehler, and a bio-inspired composition by Alberto Carretero using P-system. With my research on cell-inspired audio-visual works The Cell Planet using Touch Designer, music creation for the Protein Misfolding project, and the soft-robotic inspired biomimetic textiles and art, this paper provides solutions for integrating technology, science, engineering, and art from a deeper, experimental, and philosophical context.

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van der Deijl, Willem. "The sentience argument for experientialism about welfare". Philosophical Studies 178, nr 1 (12.02.2020): 187–208. http://dx.doi.org/10.1007/s11098-020-01427-w.

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AbstractCan a person’s degree of wellbeing be affected by things that do not enter her experience? Experientialists deny that it can, extra-experientialists affirm it. The debate between these two positions has focused on an argument against experientialism—the experience machine objection—but few arguments exist for it. I present an argument for experientialism. It builds on the claim that theories of wellbeing should not only state what constitutes wellbeing, but also which entities are welfare subjects. Moreover, the claims it makes about these two issues should have a certain coherence with each other. I argue that if we accept a particular plausible answer to the second question—namely that all and only sentient beings are welfare subjects—extra-experientialist theories face a problem of coherence. While this problem can typically be solved, doing so will involve steps that are unattractive. On experientialist theories, on the other hand, the answer to these questions cohere perfectly.

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Angel, Aprilia Christyana Tri, Viktor Handrianus Pranatawijaya i Widiatry Widiatry. "Analisis Sentimen dan Emosi dari Ulasan Google Maps Untuk Layanan Rumah Sakit di Palangka Raya Menggunakan Machine Learning". KONSTELASI: Konvergensi Teknologi dan Sistem Informasi 4, nr 1 (27.06.2024): 35–49. http://dx.doi.org/10.24002/konstelasi.v4i1.8924.

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Penelitian ini bertujuan untuk menganalisis sentimen dan emosi dari ulasan Google Maps untuk layanan rumah sakit di Palangka Raya menggunakan machine learning. Data yang digunakan dalam penelitian ini adalah ulasan dari Google Maps untuk 11 rumah sakit di Palangka Raya. Data diolah dengan preprocessing untuk membersihkan dan mempersiapkan data untuk analisis. Selanjutnya, data diklasifikasikan berdasarkan sentimen (positif, negatif, netral) dengan VADER (Valence Aware Dictionary and Sentiment Reasoner) dan emosi (seperti marah, senang, sedih, dll) menggunakan NRC Lexicon. Algoritma yang digunakan dalam penelitian ini adalah K-Nearest Neighbors (KNN), Logistic Regression, dan Decision Tree. Hasil penelitian menunjukkan bahwa ketiga algoritma tersebut memiliki performa yang berbeda-beda ketika mengklasifikasikan sentimen dan emosi dari ulasan. Algoritma Decision Tree memiliki akurasi tertinggi yaitu 92%, diikuti dengan Logistic Regression dengan akurasi 86%, dan KNN dengan akurasi 48%. Penelitian ini menunjukkan bahwa machine learning dapat digunakan untuk menganalisis sentimen dan emosi dari ulasan pada Google Maps dengan baik. This research aims to analyze the sentiment and emotion from reviews on Google Maps for hospital services in Palangka Raya using machine learning. The data used in this research was reviews from Google Maps for 11 hospitals in Palangka Raya. The data was processed using preprocessing to clean and prepare the data for analysis. Furthermore, the data was classified based on the sentiments (positive, negative, neutral) with VADER (Valence Aware Dictionary and Sentiment Reasoner) and emotions (such as angry, happy, sad, etc.) using NRC Lexicon. The algorithms used in this research are K-Nearest Neighbors (KNN), Logistic Regression, and Decision Tree. The research results show that the three algorithms have different performances when classifying sentiment and emotion from reviews. The Decision Tree algorithm has the highest accuracy of 92%, followed by Logistic Regression with an accuracy of 86%, and KNN with an accuracy of 48%. This research shows that machine learning can be used to analyze sentiment and emotion from reviews on Google Maps well.

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J, Dr Bhuvana. "A STUDY AND DEVELOPMENT OF APPLICATION ON SENTIMENT ANALYSIS". International Scientific Journal of Engineering and Management 03, nr 03 (15.03.2024): 1–7. http://dx.doi.org/10.55041/isjem01354.

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A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is its primary goal. A subfield of natural language processing called sentiment analysis is concerned with locating and obtaining subjective data from text. Analysing and categorising the emotional tone or polarity indicated in text—such as reviews, social media postings, news stories, and consumer feedback—is its primary goal. Conversely, machine learning-based techniques employ algorithms to learn from a labelled dataset and categorise fresh data according to patterns seen in the training data. Hybrid methods combine both approaches to achieve better accuracy and coverage. Machine learning models, particularly Support Vector Machines (SVM), were utilized for sentiment analysis, involving the conversion of text data into numerical feature vectors and learning a hyperplane to classify sentiments accurately. Keywords: Sentiment Analysis, Machine Learning, HTML, Support Vector Machines, Bootstrap, Visual Studio Code.

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Qiu, Xin, i Risto Miikkulainen. "Enhancing Evolutionary Conversion Rate Optimization via Multi-Armed Bandit Algorithms". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 9581–88. http://dx.doi.org/10.1609/aaai.v33i01.33019581.

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Conversion rate optimization means designing web interfaces such that more visitors perform a desired action (such as register or purchase) on the site. One promising approach, implemented in Sentient Ascend, is to optimize the design using evolutionary algorithms, evaluating each candidate design online with actual visitors. Because such evaluations are costly and noisy, several challenges emerge: How can available visitor traffic be used most efficiently? How can good solutions be identified most reliably? How can a high conversion rate be maintained during optimization? This paper proposes a new technique to address these issues. Traffic is allocated to candidate solutions using a multi-armed bandit algorithm, using more traffic on those evaluations that are most useful. In a best-arm identification mode, the best candidate can be identified reliably at the end of evolution, and in a campaign mode, the overall conversion rate can be optimized throughout the entire evolution process. Multi-armed bandit algorithms thus improve performance and reliability of machine discovery in noisy real-world environments.

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Van der Colff, M. A. "Aliens and existential elevators: absurdity and its shadows in Douglas Adams’s Hitch hiker series". Literator 29, nr 3 (25.07.2008): 123–38. http://dx.doi.org/10.4102/lit.v29i3.128.

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According to twentieth-century existentialist philosophy, the universe as we know it is steeped in senselessness, and the only possible means of survival is the construction of subjective meaning. Douglas Adams’s fictional universe portrayed in his “Hitch hiker” series reflects the arbitrary nature of existence, and the characters dwelling in this narrative space are faced with two existential choices: the one is defiance in the face of senselessness, the other is bleak despair. This article explores the existential choices made by prominent characters in the “Hitch hiker” series. The article distinguishes between and analyses the Sisyphus characters and their polar opposites (or nihilist shadows) in Douglas Adams’s “Hitch hiker” series. Adams’s characters, be they human, alien or sentient machine, all face the same existential choice: actuate individual meaning, or resort to despondency. Characters who choose the first option are regarded as Sisyphus figures, whereas characters who choose the latter are referred to as shadows or nihilist nemeses.

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Purohit, Amit. "Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques". International Journal for Research in Applied Science and Engineering Technology 9, nr VII (10.07.2021): 233–39. http://dx.doi.org/10.22214/ijraset.2021.36202.

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Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.

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Mr. V. S. Raj kumar i Dr. T. Kumaresan. "A Comparative Study on Machine Learning Approaches for Sentiment Analysis". International Research Journal on Advanced Engineering Hub (IRJAEH) 2, nr 02 (29.02.2024): 169–77. http://dx.doi.org/10.47392/irjaeh.2024.0029.

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Sentiment analysis plays a pivotal role in the operations of online product companies. User reviews are taken into account by others when they search for products, forming the cornerstone for delivering the right product based on user sentiments through sentiment analysis. Sentiment analysis involves the process of collecting, analyzing, and recommending reviews, which are often extensive and contain multiple paragraphs of content. This paper presents a comparative analysis of various machine learning models used to conduct sentiment analysis on customer reviews of Amazon products within the Electronics category. The initial models under scrutiny for our analysis include Logistic Regression, Decision Tree, Naive Bayes Classifier, Random Forest, Support Vector Machines, and BERT Model. The experimental result show that BERT classifier achieves higher accuracy when compare with other machine learning models.

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L. K. Vishwamitra, Anjali Verma ,. "A Stochastic Regression and Sentiment Analysis-Based Approach for Forecasting Trends in the Stock Market". Journal of Electrical Systems 20, nr 2 (4.04.2024): 2641–60. http://dx.doi.org/10.52783/jes.2036.

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Stochastic regression problems especially applied to time series forecasting problems often encounter the challenge of volatility and unpredictable seasonality in datasets. One such application happens to forecasting the movement of stock markets globally. Stock data across the world exhibits an intrinsic baseline noise, which often can’t be correlated to previous temporal patterns. Rather, such divergences are an effect of multiple non-numeric variables which govern stock movement such as political scenarios, trade wars, imminent economic waxing and waning trends to name a few. However, it is essential to incorporate these factors while training a model so as to design a strongly sentient and anticipatory regression algorithm which is both pervasive and robust, encompassing as many numeric and non-numeric factors as possible. While conventional machine learning and deep learning approaches tend to leverage tested model architectures, they often miss out on the optimization of both training data and training method to yield potentially high forecasting accuracy. The paper combines stochastic regression in terms of momentum based training and data optimization which is shown to exhibit considerable lower forecasting error compared to existing approaches. To encompass a wider feature space, sentiment analysis has been incorporated to correlate stock movements with public opinions. The proposed approach has been tested on a multitude of benchmark datasets, to validate the performance of the approach. The mean absolute percentage error (MAPE) and regression values have been selected as primary metrics for the performance evaluation. The proposed approach attains a mean MAPE value of 3.22%. The analysis of the MAPE and forecasting accuracy w.r.t. existing benchmark approaches proves the improved forecasting performance of the proposed approach over a period of 300 days.

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Saxena, Surbhi, Anant Deogaonkar, Rupesh Pais i Reshma Pais. "Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning". International Journal of Professional Business Review 8, nr 4 (18.04.2023): e01216. http://dx.doi.org/10.26668/businessreview/2023.v8i4.1216.

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Purpose: The objective of this study was to analyze workplace productivity through employee sentiment analysis using machine learning. Theoretical framework: A lot of literature is already published on employee productivity and sentiment analysis as a tool, but the study here is intended to address the issues in employee productivity post-COVID’19. Design/methodology/approach: The authors have studied the relationship between sentiments and workplace productivity post-COVID- 19. Sentiments were captured from the text inputs given by seventy-two survey respondents from a mid-sized consultancy firm and correlated against the productivity scores. A machine learning model was developed using Python to calculate the sentiment score. Findings: 98.6% of the respondents had a high productivity score, whereas 88.9% showed positive sentiments. The majority of the responses showed a positive correlation between positive sentiments and high productivity levels. Research, Practical and Social Implications: The study paves way for identification of action plan for productivity enhancement through sentiment analysis. Originality/Value: No previous work on employee productivity using sentiment analysis is done till now.

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Agustina, Nova, i Candra Nur Ihsan. "Pendekatan Ensemble untuk Analisis Sentimen Covid19 Menggunakan Pengklasifikasi Soft Voting". Jurnal Teknologi Informasi dan Ilmu Komputer 10, nr 2 (14.04.2023): 263. http://dx.doi.org/10.25126/jtiik.20231026215.

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Covid19 berdampak pada sektor kehidupan, mulai dari sektor ekonomi, pendidikan, kesehatan, invertasi, pariwisata hingga menimbulkan krisis lain yaitu fenomena ketakutan dan kepanikan masyarakat yang dipicu oleh informasi yang tidak lengkap dan akurat. Ketakutan dan kepanikan massa menyebabkan publik mempublikasikan sentimen di media sosial untuk memberikan tanggapan atau kritik terhadap keputusan yang dibuat oleh negara. Pandangan masyarakat terhadap Covid19 perlu dijadikan landasan sebagai pendukung keputusan untuk menyusun kebijakan pemerintah dalam menangani Covid19 di Indonesia. Penelitian ini bertujuan untuk membandingkan dan menerapkan algoritma Logistic Regression, Naïve Bayes, dan Support Vector Machine menggunakan pengklasifikasi dari ensemble, yaitu Soft Voting untuk analisis sentimen perihal Covid19 pada media sosial Twitter. Implementasi Soft Voting untuk analisis sentiment masyarakat Indonesia terhadap Covid19 menjadi kebaruan pada penelitian ini. Soft Voting akan menentukan prediksi baru berdasarkan rekomendasi maksimum dari berbagai model yang diperlukan untuk analisis sentimen. Pada penelitian ini, semua algoritma mendapatkan akurasi yang sama untuk analisis sentimen, yaitu sebesar 89%. Penerapan metode ensemble meningkatkan akurasi model untuk prediksi sentimen menjadi 91%. Abstract Covid-19 has impacted all sectors of life, ranging from the economic sector, education, health, investment, tourism to causing another crisis, i.e., the phenomenon of public fear and panic triggered by incomplete and accurate information. Fear and panic cause the public to publish sentiments on social media to provide feedback or criticism of decisions made by the state. The public's view of Covid-19 needs to be used as a basis for decision support to formulate government policies in dealing with Covid-19 in Indonesia. This study aims to compare and apply the Logistic Regression, Naïve Bayes, and Support Vector Machine algorithms using the classifier from ensemble, i.e., Soft Voting for sentiment analysis related to Covid19 on Twitter social media. The application of Soft Voting for the analysis of Indonesian public's sentiments towards Covid19 is a novelty in this research. Soft Voting will determine new predictions based on maximum recommendations from various models needed for sentiment analysis. In this study, all algorithms get the same accuracy for sentiment analysis, which is 89%. The application of the ensemble method increases the accuracy of the model for sentiment prediction by up to 91%.

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Wijaya, Yoga Vikriansyah, Adhitia Erfina i Cecep Warman. "Analisis Sentimen Seputar UU ITE Menggunakan Algoritma Support Vector Machine". Progresif: Jurnal Ilmiah Komputer 17, nr 2 (17.08.2021): 1. http://dx.doi.org/10.35889/progresif.v17i2.644.

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AbstractSentiment analysis of twitter tweets from the Indonesian people can be used as one of the parameters to be a support for the government in evaluating decision making and policies in the future. This study aims to find out the sentiments of Indonesian people's tweets on Twitter about the Information and Electronic Transaction Law. The data material used in this study uses a query on the Information and Electronic Transaction Law, Hate Speech, Defamation, Online Fraud, and Data Theft. The test is carried out by calculating accuracy, precision, recall and F1-score, using a variety of training data and test data. The highest accuracy results were obtained from the composition of 90% training data and 10% test data with an accuracy value of 84% with an average precision of 84%, recall 65%, f1-score 71% for each sentiment class.Keywords: Sentiment Analysis, Support Vector Machine Algorithm, Community TweetAbstrakAnalisis Sentimen cuitan twitter dari masyarakat Indonesia dapat dijadikan sebagai salah satu parameter untuk menjadi penunjang bagi pemerintah dalam mengevaluasi pengambilan keputusan dan kebijakan di masa yang akan datang. Penelitian ini bertujuan untuk mengetahui sentimen dari cuitan masyarakat Indonesia di twitter seputar Undang-Undang Informasi dan Transaksi Elektronik. Bahan data yang digunakan dalam penelitian ini menggunakan query Undang-Undang Informasi dan Transaksi Elektronik, Ujaran Kebencian, pencemaran nama baik, Penipuan Online, dan Pencurian data. Pengujian dilakukan dengan perhitungan akurasi, precision, recall dan F1-score, dengan menggunakan variatif data latih dan data uji. Hasil akurasi tertinggi didapatkan dari komposisi data latih 90% dan data uji 10% dengan nilai akurasi 84% dengan rata-rata precision 84%, recall 65%, f1-score 71% tiap kelas sentimen.Kata Kunci: Analisis Sentimen, Algoritma Support Vector Machine, Cuitan Masyarakat

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Jin, Congjun, Rongzheng Liu, Bangfeng Tang i Bokun Cai. "Predict FTSE100 Stock Movements Using Business News Sentiment and Machine Learning". Theoretical and Natural Science 2, nr 1 (20.02.2023): 50–55. http://dx.doi.org/10.54254/2753-8818/2/20220148.

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In order for investors to maximize their benefit by having better forecasts of the complex dynamics of the stock market, there are many factors that affect the stock market, from a company's financial ratios to investor sentiment and reactions to financial news. This project aims to collect UK business news from the Guardian and uses NLP techniques to transform unstructured text data into usable structured sentiment data to predict the movement of the FTSE100 index. The program uses two different libraries TEXTBLOB and VADER to extract sentiments from both the headlines and main bodies of the business news articles. Four machine learning algorithms including Logistic Regression, Naive Bayes, K-Nearest Neighbours and Support Vector Machines and a voting classifier were used to predict FTSE100 index movement given the business news sentiments of the previous day.

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Ilmawan, Lutfi Budi, i Edi Winarko. "Aplikasi Mobile untuk Analisis Sentimen pada Google Play". IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, nr 1 (31.01.2015): 53. http://dx.doi.org/10.22146/ijccs.6640.

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AbstrakGoogle dalam application store-nya, Google Play, saat ini telah menyediakan sekitar 1.200.000 aplikasi mobile. Dengan sejumlah aplikasi tersebut membuat pengguna memiliki banyak pilihan. Selain itu, pengembang aplikasi mengalami kesulitan dalam mencari tahu bagaimana meningkatkan kinerja aplikasinya. Dengan adanya permasalahan tersebut, maka dibutuhkan sebuah aplikasi analisis sentimen yang dapat mengolah sejumlah komentar untuk memperoleh informasi.Sistem yang dibangun memiliki tujuan untuk menentukan polaritas sentimen dari ulasan tekstual aplikasi pada Google Play yang dilakukan dari perangkat mobile. Perangkat mobile memiliki portabilitas yang tinggi dan sebagian dari perangkat tersebut memiliki resource yang terbatas. Hal tersebut diatasi dengan menggunakan arsitektur sistem berbasis client server, di mana server melakukan tugas-tugas yang berat sementara client-nya adalah perangkat mobile yang hanya mengerjakan tugas yang ringan. Dengan solusi tersebut maka Analisis sentimen dapat diaplikasikan pada mobile environment.Adapun metode klasifikasi yang digunakan adalah Naïve Bayes untuk aplikasi yang dikembangkan dan Support Vector Machine Linier sebagai pembanding. Nilai akurasi dari Naïve Bayes classifier dari aplikasi yang dibangun sebesar 83,87% lebih rendah jika dibandingkan dengan nilai akurasi dari SVM Linier classifier sebesar 89,49%. Adapun penggunaan semantic handling untuk mengatasi sinonim kata dapat mengurangi akurasi classifier. Kata kunci— analisis sentimen, google play, klasifikasi, naïve bayes, support vector machine AbstractGoogle's Google Play now providing approximately 1.200.000 mobile applications. With these number of applications, it makes the users have many options. In addition, application developers have difficulties in figuring out how to improve their application performance. Because of these problems, it is necessary to make a sentiment analysis applications that can process review comments to get valuable information.The purpose of this system is determining the polarity of sentiments from applications’s textual reviews on Google Play that can be performed on mobile devices. The mobile device has high portability and the majority of these devices have limited resource. That problem can be solved by using a client server based system architecture, where the server performs training and classification tasks while clients is a mobile device that perform some of sentiment analysis task. With this solution, the sentiment analysis can be applied to the mobile environment.The classification method that used are Naive Bayes for developed application and Linear Support Vector Machine that is used for comparing. Naïve Bayes classifier’s accuracy is 83.87%. The result is lower than the accuracy value of Linear SVM classifier that reach 89.49%. The use of semantic handling can reduce the accuracy of the classifier. Keywords—sentiment analysis, google play, classification, naïve bayes, support vector machine

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Nugroho, Dimas Dwi, Arief Setyanto i Hanif Al Fatta. "Analisis Sentimen Sekolah Online pada Twitter dengan Algoritma Support Vector Machine". Respati 17, nr 3 (10.11.2022): 38. http://dx.doi.org/10.35842/jtir.v17i3.466.

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INTISARISeiring meningkatnya masyarakat yang terdampak wabah Covid-19, pemerintah akhirnya melakukan berbagai kebijakan untuk mengurangi resiko dari wabah Covid-19, salah satunya adalah Kebijakan Sekolah Online (Belajar dari rumah). Namun menteri Pendidikan dan Kebudayaan Nadiem Makarim juga mewacanakan bahwa PJJ (Pelajaran Jarak Jauh) tetap dilakukan setelah pandemi Covid-19 sudah selesai. Dari kebijakan tersebut menimbulkan berbagai opini positif dan negatif dari masyarakat, opini tersebut dapat dilihat melalui media sosial twitter. Sentimen dan opini adalah fitur penting dari keberadaan manusia. Analisis Sentimen bermaksud untuk memahami pendapat-pendapat ini dan mendistribusikannya ke dalam kategori seperti positif, netral dan negatif. Analisis sentiment saat ini terus berkembang dengan berbagai methode dan algoritma yang ada. Berdasarkan beberapa penelitian yang ada diketahui bahwa dengan menggunakan metode Algoritma Support Vector Machine dapat memberikan hasil akurasi yang lebih baik dari pada Algoritma yang lain.. Hasil penelitian dari 1200 Data tweet diperoleh Jumlah tweet netral sebanyak 445, tweet positif sebanyak 396 dan tweet negatif sebanyak 359 tweet. Dari data tersebut kemudian diproses menggunakan algoritma Support Vector Machine dan mendapatkan hasil nilai accuracy sebesar 82%, nilai Precision 83%, nilai Recall 82% dan nilai F1-Score 82 %., maka dapat disimpulkan metode Algoritma Support Vector Machine (SVM) dinilai lebih relevan untuk diterapkan pada penelitian sentiment analisis.Kata kunci— Sentimen Analisis, SVM, Covid-19, Sekolah Online, Scrawling Twitter. ABSTRACTAlong with the increasing number of people affected by the Covid-19 outbreak, the government has finally implemented various policies to reduce the risk of the Covid-19 outbreak, one of which is the Online School Policy (Learning from home). However, the Minister of Education and Culture Nadiem Makarim also discoursed that PJJ (Distance Learning) would still be carried out after the Covid-19 pandemic was over. From this policy, it raises various positive and negative opinions from the public, these opinions can be seen through Twitter social media. Sentiments and opinions are essential features of human existence. Sentiment Analysis intends to understand these opinions and distribute them into categories such as positive, neutral and negative. Sentiment analysis is currently growing with various existing methods and algorithms. Based on several existing studies, it is known that using the Support Vector Machine Algorithm method can provide better accuracy results than other algorithms. The results of the 1200 tweet data obtained were 445 neutral tweets, 396 positive tweets and 359 negative tweets. tweets. From this data, it is processed using the Support Vector Machine algorithm and gets an accuracy value of 82%, Precision value 83%, Recall value 82% and F1-Score value 82%., it can be concluded that the Support Vector Machine (SVM) Algorithm method is considered more relevant to be applied to sentiment analysis research..Kata kunci— Analysis Sentiment, SVM, Covid-19, Online School, Scrawling Twitter.

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Timur, Yan Putra, Ririn Tri Ratnasari, Anwar Allah Pitchay i Usman Jamilu. "Investigating Netizen Sentiment Toward Halal Certification in Indonesia Using Machine Learning". Jurnal Ekonomi Syariah Teori dan Terapan 10, nr 6 (30.11.2023): 525–40. http://dx.doi.org/10.20473/vol10iss20236pp525-540.

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ABSTRACT This study examined the most discussed halal certification terms and topics on Indonesian Twitter. This study also explored the sentiments and opinions of Indonesian netizens on halal certification. This analysis uses Twitter tweets from April 10 to 24, 2023. A quantitative method was used by using Orange Data Mining. Researchers used the keywords "Sertifikasi Halal", "Label Halal" and "Halal MUI" to obtain 1,000 tweets. The results of the study show that the tweets with the highest number of likes and retweets discuss the ease of obtaining Halal certification, which is guaranteed by the Cipta Kerja Law. In addition, the words "halal", "certification", and "MUI" are the most frequently used words in Twitter conversations. The most discussed topics by netizens about halal certification include "halal", "certification", "food", "MUI", "product", "sucofindo", "permit", and "safe". Twitter is dominated by tweets with neutral sentiments shown by joy emotions, as much as 68.22% of the total tweets. Keywords: Halal certification, sentiment analysis, Twitter, Machine learning, Orange data mining ABSTRAK Penelitian ini bertujuan untuk menemukan kata-kata dan topik yang paling banyak dibicarakan tentang sertifikasi halal di Twitter Indonesia. Serta untuk menemukan sentimen dan emosi netizen Indonesia tentang sertifikasi halal. Data penelitian ini berasal dari tweet Twitter yang diambil dari 10 April 2023 hingga 24 April 2023. Metode kualitatif dilakukan dengan bantuan Orange Data Mining. Peneliti menggunakan kata kunci “Sertifikasi Halal”, “Label Halal”, dan “Halal MUI” untuk mendapatkan 1.000 tweet. Hasil penelitian menunjukkan bahwa tweet yang memiliki jumlah like dan retweet terbanyak membahas tentang kemudahan perizinan sertifikasi halal yang dijamin dalam UU Cipta Kerja. Selain itu, kata "Halal", "Sertifikasi", dan "MUI" merupakan kata yang paling sering muncul dalam percakapan di Twitter. Topik yang paling banyak diperbincangkan oleh warganet mengenai sertifikasi halal antara lain topik "halal", "sertifikasi", "makanan", "MUI", "produk", "sucofindo", "izin", dan "aman". Twitter didominasi oleh tweet dengan sentimen netral yang ditunjukkan dengan emosi kegembiraan sebanyak 68,22% dari total tweet. Kata Kunci: Sertifikasi Halal, Sentiment Analisis, Twitter, Machine Learning, Orange Data Mining

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Steven, Cristian, i Wella Wella. "The Right Sentiment Analysis Method of Indonesian Tourism in Social Media Twitter". IJNMT (International Journal of New Media Technology) 7, nr 2 (28.12.2020): 102–10. http://dx.doi.org/10.31937/ijnmt.v7i2.1732.

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The growth of social media is changing the way humans communicate with each other, many people use social media such as Twitter to express opinions, experiences and other things that concern them, where things like this are often referred to as sentiments. The concept of social media is now the focus of business people to find out people's sentiments about a product or place that will become a business. Sentiment Analysis or often also called opinion mining is a computational study of people's opinions, appraisal, and emotions through entities, events and attributes owned. Sentiment analysis itself has recently become a popular topic for research because sentiment analysis can be applied in many industrial sectors, one of which is the tourism industry in Indonesia. To be able to do a sentiment analysis requires mastery of several techniques such as techniques for doing text mining, machine learning and natural language processing (NLP) to be able to process large and unstructured data coming from social media. Some methods that are often used include Naive Bayes, Neural Networks, K-Nearest Neighbor, Support Vector Machines, and Decision Tree. Because of this, this research will compare these four algorithms so that an algorithm can be used to analyze people's sentiments towards the city of Bali.

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Jadad, Alejandro R., i Tamen M. Jadad Garcia. "From a Digital Bottle: A Message to Ourselves in 2039". Journal of Medical Internet Research 21, nr 11 (1.11.2019): e16274. http://dx.doi.org/10.2196/16274.

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We are fully aware that we could have wasted our time writing this message, as nobody might read it. Even those who read it might ignore it, and those who read and care about it might be unable to do anything. It may simply be too late. Nevertheless, this message describes the hopes we had back in 1999, imagining how the incredible digital tools whose birth we were witnessing, could change the world for the better. In 2019, when we wrote these words, we were saddened to realize that most of what we had imagined and proposed in the past 20 years could have been written the day before, without losing an iota of relevance. Whoever or whatever you might be, dear reader—a human, a sentient machine, or a hybrid—we would like you to understand that, rather than an attempt to predict the future, which probably continues to be an impossible endeavor, this message was meant to act as an invitation, regardless of when or where it is found, to engage in a conversation that has already transcended time and space, even if the issues it contains have become irrelevant.

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Miguel Garay Gallastegui, Luis, Ricardo Reier Forradellas i Sergio Luis Náñez Alonso. "Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques". Innovative Marketing 20, nr 2 (17.04.2024): 100–115. http://dx.doi.org/10.21511/im.20(2).2024.09.

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In the digital era, understanding public sentiment toward brands on social media is essential for crafting effective marketing strategies. This study applies sentiment analysis on Banco Bilbao Vizcaya Argentaria (BBVA) tweets using advanced machine learning techniques, particularly the eXtreme Gradient Boosting (XGBoost) algorithm, which showed remarkable precision (91.2%) in sentiment classification. This process involved a systematic approach to data collection, cleaning, and preprocessing. The precision of XGBoost highlights its effectiveness in analyzing social media conversations about banking. Additionally, this paper achieved improvements in neutral tweet classification, with accuracy rates at 87-88% and a reduced misclassification rate, enhancing the analysis reliability. The findings not only uncover general sentiments toward BBVA but also provide insight into how these sentiments shift in response to marketing activities and global events. This gives marketers a valuable tool for real-time assessment of campaign effectiveness and brand perception. Ultimately, employing the XGBoost algorithm for sentiment analysis offers BBVA a strategic advantage in understanding and engaging its online audience, demonstrating the significant benefits of using sophisticated machine learning in banking. The study emphasizes the crucial role of data-driven sentiment analysis in developing informed business strategies and improving customer relationships in the banking industry’s competitive landscape.

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Wang, Xinzhi, Hui Zhang i Zheng Xu. "Public Sentiments Analysis Based on Fuzzy Logic for Text". International Journal of Software Engineering and Knowledge Engineering 26, nr 09n10 (listopad 2016): 1341–60. http://dx.doi.org/10.1142/s0218194016400076.

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Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

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R.J, Prof Dhannal ,. "Sentiment Analysis on Placement Aspect". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 04 (20.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem31121.

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Sentiment analysis has emerged as a pivotal tool within the realm of natural language processing, finding widespread application across diverse domains such as marketing, social media analysis, and customer feedback assessment. This research delves into the specialized domain of sentiment analysis focused on the placement aspect, which revolves around the intricate process of aligning individuals with suitable job opportunities. The primary objective of this study is to dissect sentiments embedded within textual data associated with placements, including resumes, job listings, and interview appraisals, with the aim of extracting nuanced insights to inform strategic decision-making within recruitment and talent acquisition processes. Leveraging cutting-edge techniques in machine learning and natural language processing, our research employs a multifaceted approach encompassing text preprocessing, feature extraction, and sentiment classification methodologies. A spectrum of sentiment analysis algorithms is explored, ranging from lexicon-based methods to sophisticated machine learning models (such as Support Vector Machines and Naive Bayes) and deep learning architectures (including Recurrent Neural Networks and Transformers). Additionally, our investigation extends to assessing the influence of various factors—such as dataset dimensions, domain specificity, and feature selection techniques—on the accuracy and robustness of sentiment analysis outcomes.

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Khai Tran i Thi Phan. "Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying". Applied Sciences 9, nr 13 (8.07.2019): 2760. http://dx.doi.org/10.3390/app9132760.

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Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.

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Ragupathy, R., i Lakshmana Phaneendra Maguluri. "Comparative analysis of machine learning algorithms on social media test". International Journal of Engineering & Technology 7, nr 2.8 (19.03.2018): 284. http://dx.doi.org/10.14419/ijet.v7i2.8.10425.

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Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in main text. It mainly refers to a text classification. Social media is generating a vast amount of sentiment rich data in the form of tweets, blog posts, comments, status updates, news etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the public. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, Machine learning approach has been used for the sentiment analysis of movie review dataset and is analysed by Naïve Bayes, Decision tree, KNN, and SVM classifiers. Commencing the most efficient classification technique is the moto of the paper. Efficiency of the classifier is decided based on some regular parameters that are outputs of the classification techniques.

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Tittu T, Anoush, Rakshitha K, Nanditha TN, Sireya Rani M i Yukthi S R. "STOCK MARKET ANALYSIS". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 01 (15.01.2024): 1–10. http://dx.doi.org/10.55041/ijsrem28108.

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The abstract aims to address the correlation between stock market movements and public sentiments expressed on Twitter. It delves into the utilization of sentiment analysis and supervised machine learning techniques to explore this connection. The study leverages Word2vec for textual representation, examining how shifts in stock prices align with sentiments expressed in tweets about specific companies. The investigation underscores the potential impact of positive news and social media sentiments on stock prices, emphasizing a demonstrated correlation between fluctuations in stock prices and sentiments conveyed in Twitter.- Keywords: Hashtag Collection, Data Collection, Real-Time Stock History Data, Positive Keywords, Negative Keywords, Polarity Computation, Sentiment Analysis , Sentiment Index Computation, Sentiment Discrepancy Index, Price Prediction, Yahoo Finance API

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Chen, Shaowei, Yu Wang, Jie Liu i Yuelin Wang. "Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 14 (18.05.2021): 12666–74. http://dx.doi.org/10.1609/aaai.v35i14.17500.

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Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and sentiment classification, it is critical and challenging to appropriately capture and utilize the associations among them. In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge. Specifically, we devise three types of queries, including non-restrictive extraction queries, restrictive extraction queries and sentiment classification queries, to build the associations among different subtasks. Furthermore, considering that an aspect sentiment triplet can derive from either an aspect or an opinion expression, we design a bidirectional MRC structure. One direction sequentially recognizes aspects, opinion expressions, and sentiments to obtain triplets, while the other direction identifies opinion expressions first, then aspects, and at last sentiments. By making the two directions complement each other, our framework can identify triplets more comprehensively. To verify the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets. The experimental results demonstrate that BMRC achieves state-of-the-art performances.

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Eko Saputro, Wahyu, Haris Yuana i Wahyu Dwi Puspitasari. "ANALISIS SENTIMEN PENGGUNA DOMPET DIGITAL DANA PADA KOLOM KOMENTAR GOOGLE PLAY STORE DENGAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE". JATI (Jurnal Mahasiswa Teknik Informatika) 7, nr 2 (25.08.2023): 1151–56. http://dx.doi.org/10.36040/jati.v7i2.6842.

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Dompet digital dana merupakan sebuah alat transaksi elektronik yang di gunakan untuk berbagai macam pembayaran, respon pro dan kontra oleh masyarakat terhadap aplikasi dompet digital DANA tersebut banyak di luncurkan di kolom komentar yang ada di google play strore. Opini para pengguna aplikasi ini dapat di klasifikasikan menjadi komentar bersentimen positif dan negatif. Tujuan penelitian ini adalah untuk mengetahui sentimen positif dan negatif dari pengguna aplikasi dompet digital DANA. Maka dari itu peneliti menggunakan algoritma Suport Vector Machine (SVM) dalam analisis sentimen tentang apliaksi dompet digital DANA. Hasil penelitian menujukan bahwa sebanyak 35% pengguna aplikasi DANA memiiliki sentiment podsitif sedangkan sebanyak 65% pengguna aplikasi DANA memiliki sentiment negatif berdasarkan pengujian klasifikasi SVM memiliki akurasi sebesar 80%, precision sebesar 84.06% untuk sentimen negatif dan 74.08% untuk sentiment positif, serta recall sebesar 87.02% untuk sentimen negatif, serta recall sebesar 69.21% untuk sentiment positif.

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M, Vasuki. "Enhancing Social Media Insights: Leveraging Artificial Intelligence for Sentiment Analysis". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 05 (16.05.2024): 1–5. http://dx.doi.org/10.55041/ijsrem33351.

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Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

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Elshafy, Mohamed Fathy Abd, Dr Tarek Aly i Prof Mervat Gheith. "Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques". International Journal for Research in Applied Science and Engineering Technology 12, nr 4 (30.04.2024): 2007–27. http://dx.doi.org/10.22214/ijraset.2024.60250.

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Abstract: Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

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Rokade, Prakash Pandharinath, i Aruna Kumari D. "Business recommendation based on collaborative filtering and feature engineering – aproposed approach". International Journal of Electrical and Computer Engineering (IJECE) 9, nr 4 (1.08.2019): 2614. http://dx.doi.org/10.11591/ijece.v9i4.pp2614-2619.

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Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle. The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.

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Goswami, Mausumi, i Ahini Abraham. "Towards Sustainable Living through Sentiment Analysis during Covid19". ECS Transactions 107, nr 1 (24.04.2022): 18569–82. http://dx.doi.org/10.1149/10701.18569ecst.

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Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyze the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis.

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Zakaria, Zakaria, Kusrini Kusrini i Dhani Ariatmanto. "Sentiment Analysis to Measure Public Trust in the Government Due to the Increase in Fuel Prices Using Naive Bayes and Support Vector Machine". International Journal of Artificial Intelligence & Robotics (IJAIR) 5, nr 2 (24.11.2023): 54–62. http://dx.doi.org/10.25139/ijair.v5i2.7167.

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The study examines public sentiment on the government's fuel price policy using an experimental approach and Twitter data obtained through API scraping. It applies sentiment analysis methods like Naïve Bayes, SVM, and Majority Voting. SVM achieved 85% accuracy, excelling in identifying negative sentiments, while Majority Voting reached 70% by considering confidence levels. Naïve Bayes struggled with neutral sentiments. They are combining methods to enhance the understanding of public sentiments on fuel price changes. The study highlights sentiment analysis' effectiveness in gauging reactions to fuel policies, with SVM offering more profound insights into sentiments related to fuel price hikes. Challenges remain in identifying neutral sentiments due to social media text brevity. These findings underscore the contextual importance of interpreting sentiment analysis. Leveraging these insights, governments can understand public perceptions better and devise improved communication strategies for sensitive economic policies like fuel price hikes, fostering better government-citizen interactions. The study aims to guide stakeholders in comprehending public perspectives within public policy, emphasizing the relevance of sentiment analysis for policy evaluation.

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