Добірка наукової літератури з теми "Sentiment analytics"
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Статті в журналах з теми "Sentiment analytics"
Rokade, Prakash P., and Aruna Kumari D. "Business intelligence analytics using sentiment analysis-a survey." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 613. http://dx.doi.org/10.11591/ijece.v9i1.pp613-620.
Повний текст джерелаBAKİROV, Aslan, Kevser Nur ÇOĞALMIŞ, and Ahmet BULUT. "Scalable sentiment analytics." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 1560–70. http://dx.doi.org/10.3906/elk-1311-128.
Повний текст джерелаHuang, Changqin, Zhongmei Han, Ming Li, Xizhe Wang, and Wenzhu Zhao. "Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis." Australasian Journal of Educational Technology 37, no. 2 (May 10, 2021): 81–95. http://dx.doi.org/10.14742/ajet.6749.
Повний текст джерелаAli, G. G. Md Nawaz, Md Mokhlesur Rahman, Md Amjad Hossain, Md Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, and Jim Samuel. "Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics." Healthcare 9, no. 9 (August 27, 2021): 1110. http://dx.doi.org/10.3390/healthcare9091110.
Повний текст джерелаRokade, Prakash Pandharinath, and Aruna Kumari D. "Business recommendation based on collaborative filtering and feature engineering – aproposed approach." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2614. http://dx.doi.org/10.11591/ijece.v9i4.pp2614-2619.
Повний текст джерелаGukanesh, A. V., G. Karthick Kumar, and K. Karthik Raja Kumar N. Saranya. "Twitter Data Analytics – Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.
Повний текст джерелаRaman, Ramakrishnan, Sandeep Bhattacharya, and Dhanya Pramod. "Predict employee attrition by using predictive analytics." Benchmarking: An International Journal 26, no. 1 (February 4, 2019): 2–18. http://dx.doi.org/10.1108/bij-03-2018-0083.
Повний текст джерелаSingh, Amit, Mamata Jenamani, Jitesh Thakkar, and Yogesh K. Dwivedi. "A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews." Journal of Global Information Management 30, no. 8 (September 1, 2021): 1–26. http://dx.doi.org/10.4018/jgim.304069.
Повний текст джерелаHao, Jin-Xing, Yu Fu, Cathy Hsu, Xiang (Robert) Li, and Nan Chen. "Introducing News Media Sentiment Analytics to Residents’ Attitudes Research." Journal of Travel Research 59, no. 8 (November 8, 2019): 1353–69. http://dx.doi.org/10.1177/0047287519884657.
Повний текст джерелаFu, Yu, Jin-Xing Hao, Xiang (Robert) Li, and Cathy H. C. Hsu. "Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News." Journal of Travel Research 58, no. 4 (May 16, 2018): 666–79. http://dx.doi.org/10.1177/0047287518772361.
Повний текст джерелаДисертації з теми "Sentiment analytics"
Doherty, Amy Josephine. "Understanding Web Sentiment Analytics and Visualization, A Social Media Analysis." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/320061.
Повний текст джерелаShen, Chao. "Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1739.
Повний текст джерелаYu, Xiang. "Analysis of new sentiment and its application to finance." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9062.
Повний текст джерелаBouayad, Lina. "Analytics and Healthcare Costs (A Three Essay Dissertation)." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5876.
Повний текст джерелаPiksina, Olga, and Patricia Vernholmen. "Coronavirus-Related Sentiment and Stock Prices : Measuring Sentiment Effects on Swedish Stock Indices." Thesis, KTH, Fastigheter och byggande, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276759.
Повний текст джерелаDenna studie undersöker den effekt coronavirus-relaterat sentiment haft på avkastningen på svenska aktieindex under coronaviruspandemin. Vi studerar avkastningen på large cap- och small cap-prisindexen OMXSLCPI och OMXSSCPI under perioden 2 januari 2020 – 30 april 2020. Proxier för coronavirus-sentiment konstrueras från nyhetsartiklar som klustrats i ämnen genom latent Dirichlet-allokering och poängsatts genom sentimentanalys. Sentimentproxiernas påverkan på aktieindexen mäts sedan med en dynamisk multipel regressionsmodell. Resultaten visar att proxierna som representerar fundamentala förändringar i vår modell — svensk politik och ekonomisk policy — har en starkt signifikant inverkan på avkastningen på båda indexen, vilket är konsekvent med finansiell teori. Vi finner även att sentimentproxierna sport och spridning av coronaviruset är statistiskt signifikanta i sin påverkan på svenska aktiepriser. Detta innebär att coronavirus-relaterade nyheter påverkade marknadssentiment i Sverige under undersökningsperioden och skulle kunna användas för att upptäcka arbitrage. Slutligen visas mängden sentimentframkallande nyheter publicerade per dag ha en inverkan på aktieprisvolatilitet.
Aring, Danielle C. "Integrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1494359605127555.
Повний текст джерелаBelau, Francini Scipioni. "Uma proposta de representação linguístico-computacional da negação com vistas à análise de sentimentos em contexto de ensino e aprendizagem on-line." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6090.
Повний текст джерелаMade available in DSpace on 2017-03-15T16:53:11Z (GMT). No. of bitstreams: 1 Francini Scipioni Belau_.pdf: 2278562 bytes, checksum: 806e6ee479b7b02ba595eb0759a37f05 (MD5) Previous issue date: 2017-01-11
Gvdasa - Inteligência Educacional
A temática deste trabalho estabelece um diálogo entre as áreas da educação a distância, linguística e processamento automático das línguas naturais (PLN). A proposta é responder às seguintes questões norteadoras: (i) como a negação da emoção se manifesta na superfície da língua? E (ii) que regras computacionais expressam a negação da emoção?. A metodologia do trabalho segue o proposto por Dias-da-Silva (2006), que organiza os trabalhos em PLN em três domínios de investigação complementares: (i) linguístico, (ii) linguístico-computacional e (iii) computacional. No primeiro domínio, o linguístico, descreve-se o fenômeno da negação e o seu uso. No domínio linguístico-computacional, vamos representar os padrões percebidos para orientar os especialistas a codificarem essas regras em uma linguagem computacional. Para propor a descrição linguístico-computacional dos modos de expressão da negação, a partir de um corpus construído em contexto de ensino a distância com base nos relatos diários e fóruns dos alunos, utilizamos como base a teoria abordada por Maria Helena de Moura Neves (2011). A etapa computacional, que prevê a implementação do sistema, é própria do informata e não será contemplada neste trabalho, será realizada por grupo de pesquisa parceiro em colaboração com a empresa GVDasa. Ao todo foram criadas 11 regras linguístico-computacionais que possibilita dar conta das propriedades linguísticas identificadas ao responder a questão (i) de pesquisa. As regras visam a contribuir para que um sistema computacional possa localizar os fenômenos da negação em textos e verificar a existência de inversões de polaridade e emoção.
The thematic of this work establishes a dialogue between the fields of distance learning, linguistics, and natural language processing (NLP). The proposal is to answer the following guiding questions: (i) how does the negation of emotion manifest itself on the surface of the language? and (ii) which computational rules express the negation of emotion? The methodology of this work follows the proposed by Dias-da-Silva (2006), who organizes the works in NLP in three complementary domains of investigation: (i) linguistics, (ii) computational-linguistics, and (iii) computational. In the first domain, the linguistic domain, the phenomenon of denial and its use is described. In the linguistic-computational domain, we will represent the perceived patterns in order to guide the experts to encode these rules in computational language. In order to propose the linguistic-computational description of the forms of expression of negation, through a corpus built in a distance learning context based on daily reports and students’ forums, we take as a base the theory approached by Maria Helena de Moura Neves (2011). The computational phase which forecasts the implementation of the system is pertinent to the computing technician and it will not be contemplated in this work, but it will be performed by a partner research group in collaboration with the GVDasa company. Altogether, 11 linguistic-computational rules were created that make it possible to account for the linguistic properties identified when answering the research question (i). The rules aim to contribute with a computational system to locate the phenomenon of negation in texts and verify the existence of inversions of polarity and emotion.
Bin, Saip Mohamed A. "Big Social Data Analytics: A Model for the Public Sector." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18352.
Повний текст джерелаUniversiti Utara Malaysia
Ruan, Yiye. "Joint Dynamic Online Social Network Analytics Using Network, Content and User Characteristics." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420765022.
Повний текст джерелаRenault, Thomas. "Three essays on the informational efficiency of financial markets through the use of Big Data Analytics." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E009/document.
Повний текст джерелаThe massive increase in the availability of data generated everyday by individuals on the Internet has made it possible to address the predictability of financial markets from a different perspective. Without making the claim of offering a definitive answer to a debate that has persisted for forty years between partisans of the efficient market hypothesis and behavioral finance academics, this dissertation aims to improve our understanding of the price formation process in financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the in-formational role of financial markets, and determine the relation between the level of posting activity on social media and small-capitalization stock returns. The first essay proposes a methodology to construct a novel indicator of investor sentiment by analyzing an extensive dataset of user-generated content published on the social media platform Stock-Twits. Examining users’ self-reported trading characteristics, the essay provides empirical evidence of sentiment-driven noise trading at the intraday level, consistent with behavioral finance theories. The second essay proposes a methodology to measure investor attention to news in real-time by combining data from traditional newswires with the content published by experts on the social media platform Twitter. The essay demonstrates that news that garners high attention leads to large and persistent change in trading activity, volatility, and price jumps. It also demonstrates that the pre-announcement effect is reduced when corrected newswire timestamps are considered. The third essay provides new insights into the empirical literature on small capitalization stocks market manipulation by examining a novel dataset of messages published on the social media plat-form Twitter. The essay proposes a novel methodology to identify suspicious behaviors by analyzing interactions between users and provide empirical evidence of suspicious stock recommendations on social media that could be related to market manipulation. The conclusion of the essay should rein-force regulators’ efforts to better control social media and highlights the need for a better education of individual investors
Книги з теми "Sentiment analytics"
People, Sentiment and Social Network Analytics with Excel. Independently Published, 2019.
Знайти повний текст джерелаKumar, R. Analytical Approach-Sentimental Education. Lulu Press, Inc., 2010.
Знайти повний текст джерелаDa Costa, Dia. Introduction. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040603.003.0001.
Повний текст джерелаЧастини книг з теми "Sentiment analytics"
Sarkar, Dipanjan. "Sentiment Analysis." In Text Analytics with Python, 567–629. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_9.
Повний текст джерелаKagan, Vadim, Edward Rossini, and Demetrios Sapounas. "Text Analytics." In Sentiment Analysis for PTSD Signals, 21–32. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-3097-1_4.
Повний текст джерелаDominik, Hofer. "Sentiment Analysis." In Data Science – Analytics and Applications, 111–12. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-19287-7_17.
Повний текст джерелаZadrozny, Peter, and Raghu Kodali. "Sentiment Analysis." In Big Data Analytics Using Splunk, 255–82. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5762-2_14.
Повний текст джерелаShi, Yong. "Sentiment Analysis." In Advances in Big Data Analytics, 423–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3607-3_7.
Повний текст джерелаGarg, Yogesh, and Niladri Chatterjee. "Sentiment Analysis of Twitter Feeds." In Big Data Analytics, 33–52. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13820-6_3.
Повний текст джерелаSarkar, Dipanjan. "Semantic and Sentiment Analysis." In Text Analytics with Python, 319–76. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2388-8_7.
Повний текст джерелаAnandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Learning-Based Sentiment Analysis Using RapidMiner." In Practical Text Analytics, 243–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_15.
Повний текст джерелаAnandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Modeling Text Sentiment: Learning and Lexicon Models." In Practical Text Analytics, 151–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_10.
Повний текст джерелаAnandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Sentiment Analysis of Movie Reviews Using R." In Practical Text Analytics, 193–220. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_13.
Повний текст джерелаТези доповідей конференцій з теми "Sentiment analytics"
Pikatza-Gorrotxategi, Naiara, Izaskun Alvarez-Meaza, Rosa María Río-Belver, and Enara Zarrabeitia-Bilbao. "News versus Corporate Reputation: Measuring through Sentiment and financial analysis." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15040.
Повний текст джерелаToivanen, Ida, Venla Räsänen, Jari Lindroos, Tomi Oinas, and Sakari Taipale. "Implementing sentiment analysis to an open-ended questionnaire: Case study of digitalization in elderly care during COVID-19." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15089.
Повний текст джерелаJeon, Ye-Seul, Eun-Young Jang, and Hwan-Seok Jang. "SENTIMENT SCORES OF SENTIMENT KEYWORDS: ANALYSIS OF HOTEL REVIEW DATA." In International Conference Big Data Analytics, Data Mining and Computational Intelligence 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/bigdaci2019_201907p031.
Повний текст джерелаChen, Jinyan, Susanne Becken, and Bela Stantic. "Sentiment Analytics of Chinese Social Media Posts." In the 8th International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3227609.3227680.
Повний текст джерелаS, Manish Venkat, Parimi Mastan Rao, and Shekar Babu. "Evaluating Social Responsible Attitudes and Opinions using Sentiment Analysis – An Indian Sentiment." In 2022 3rd International Conference on Computing, Analytics and Networks (ICAN). IEEE, 2022. http://dx.doi.org/10.1109/ican56228.2022.10007315.
Повний текст джерелаDabholkar, Salil, Yuvraj Patadia, and Prajyoti Dsilva. "Automatic Document Summarization using Sentiment Analysis." In ICIA-16: International Conference on Informatics and Analytics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2980258.2980362.
Повний текст джерелаMarcucci, Juri, Giuseppe Bruno, Attilio Mattiocco, Marco Scarnò, and Donatella Sforzini. "The Sentiment Hidden in Italian Texts Through the Lens of A New Dictionary." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8580.
Повний текст джерелаDussoye, Hirikesh, and Zarine Cadersaib. "Sentiment analytics framework integrating Twitter and Odoo ERP." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8285994.
Повний текст джерелаChen, Chao, Fuhai Chen, Donglin Cao, and Rongrong Ji. "A Cross-media Sentiment Analytics Platform For Microblog." In MM '15: ACM Multimedia Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2733373.2807398.
Повний текст джерелаAnusha, M., and R. Leelavathi. "Analysis on Sentiment Analytics Using Deep Learning Techniques." In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2021. http://dx.doi.org/10.1109/i-smac52330.2021.9640790.
Повний текст джерела