Relevant bibliographies by topics / Sentiment analytics

Academic literature on the topic 'Sentiment analytics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Sentiment analytics"

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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.

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Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.
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BAKİROV, 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.

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Huang, 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.

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Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.
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Ali, 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.

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There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.
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Rokade, 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.

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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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Gukanesh, 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.

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Raman, 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.

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PurposeResearch questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.Design/methodology/approachThe email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.FindingsThose faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.Research limitations/implicationsFaculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.Practical implicationsBusiness schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.Originality/valueAlthough past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.
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Singh, 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.

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Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.
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Hao, 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.

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The progress in sentiment analytics and communication research provides a powerful scaffold by which to reexamine the long-debated research on residents’ attitudes toward tourism. To mitigate the limitations of the classical survey-based research method, this study takes a news media sentiment analytics perspective to unveil how the residents’ attitudes toward tourism evolve over time and how socioeconomic factors interact with such evolving attitudes in the context of Hong Kong. Drawn on a news data set containing 72,755 news articles published in Chinese language newspapers, this study computes the overall news sentiments for 156 calendar months since 2003, examines the face validity and nomological validity of the results, and discusses the long-run dynamics between residents’ attitudes and typical socioeconomic factors. This study adds a vital dimension to current residents’ attitudes research and practices from data-scarce to data-rich studies and from static snapshots to dynamic unfolding.
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Fu, 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.

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Sentiment analytics, as a computational method to extract emotion and detect polarity, has gained increasing attention in tourism research. However, issues regarding how to properly apply sentiment analytics are seldom addressed in the tourism literature. This study addresses such methodological challenges by employing the metalearning perspective to examine the design effects on predictive accuracy using a sentiment analysis experiment for Chinese travel news. Our results reveal strong interactions among key design factors of sentiment analytics on predictive accuracy; accordingly, this study formulates a metalearning framework to improve predictive accuracy for computational tourism research. Our study attempts to highlight and improve the methodological relevance and appropriateness of sentiment analytics for future tourism studies.
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Dissertations / Theses on the topic "Sentiment analytics"

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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.

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Shen, Chao. "Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1739.

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In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.
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Yu, Xiang. "Analysis of new sentiment and its application to finance." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9062.

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We report our investigation of how news stories influence the behaviour of tradable financial assets, in particular, equities. We consider the established methods of turning news events into a quantifiable measure and explore the models which connect these measures to financial decision making and risk control. The study of our thesis is built around two practical, as well as, research problems which are determining trading strategies and quantifying trading risk. We have constructed a new measure which takes into consideration (i) the volume of news and (ii) the decaying effect of news sentiment. In this way we derive the impact of aggregated news events for a given asset; we have defined this as the impact score. We also characterise the behaviour of assets using three parameters, which are return, volatility and liquidity, and construct predictive models which incorporate impact scores. The derivation of the impact measure and the characterisation of asset behaviour by introducing liquidity are two innovations reported in this thesis and are claimed to be contributions to knowledge. The impact of news on asset behaviour is explored using two sets of predictive models: the univariate models and the multivariate models. In our univariate predictive models, a universe of 53 assets were considered in order to justify the relationship of news and assets across 9 different sectors. For the multivariate case, we have selected 5 stocks from the financial sector only as this is relevant for the purpose of constructing trading strategies. We have analysed the celebrated Black-Litterman model (1991) and constructed our Bayesian multivariate predictive models such that we can incorporate domain expertise to improve the predictions. Not only does this suggest one of the best ways to choose priors in Bayesian inference for financial models using news sentiment, but it also allows the use of current and synchronised data with market information. This is also a novel aspect of our work and a further contribution to knowledge.
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Bouayad, Lina. "Analytics and Healthcare Costs (A Three Essay Dissertation)." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5876.

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Both literature and practice have looked at different strategies to diminish healthcare associated costs. As an extension to this stream of research, the present three paper dissertation addresses the issue of reducing elevated healthcare costs using analytics. The first paper looks at extending the benefits of auditing algorithms from mere detection of fraudulent providers to maximizing the deterrence from inappropriate behavior. Using the structure of the physicians' network, a new auditing algorithm is developed. Evaluation of the algorithm is performed using an agent-based simulation and an analytical model. A case study is also included to illustrate the application of the algorithm in the warranty domain. The second paper relies on experimental data to build a personalized medical recommender system geared towards re-enforcing price-sensitive prescription behavior. The study analyzes the impact of time pressure, and procedure cost and prescription prevalence/popularity on the physicians' use of the system's recommendations. The third paper investigates the relationship between patients' compliance and healthcare costs. The study includes a survey of the literature along with a longitudinal analysis of patients' data to determine factors leading to patients' non-compliance, and ways to alleviate it.
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Piksina, 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.

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This thesis examines the effect of coronavirus-related sentiment on Swedish stock market returns during the coronavirus pandemic. We study returns on the large cap and small cap price indices OMXSLCPI and OMXSSCPI during the period January 2, 2020 – April 30, 2020. Coronavirus sentiment proxies are constructed from news articles clustered into topics using latent Dirichlet allocation and scored through sentiment analysis. The impact of the sentiment proxies on the stock indices is then measured using a dynamic multiple regression model. The results show that the proxies representing fundamental changes in our model — Swedish Politics and Economic Policy — have a strongly significant impact on the returns of both indices, which is consistent with financial theory. We also find that sentiment proxies Sport and Coronavirus Spread are statistically significant and impact Swedish stock prices. This implies that coronavirus-related news influenced market sentiment in Sweden during the research period and could be exploited to uncover arbitrage. Finally, the amount of sentiment-inducing news published daily is shown to have an impact on stock price volatility.
Denna 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.
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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.

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Belau, 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.

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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-03-15T16:53:11Z No. of bitstreams: 1 Francini Scipioni Belau_.pdf: 2278562 bytes, checksum: 806e6ee479b7b02ba595eb0759a37f05 (MD5)
Made 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.
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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.

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The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theory
Universiti Utara Malaysia
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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.

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Renault, 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.

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L’augmentation massive du volume de données générées chaque jour par les individus sur Internet offre aux chercheurs la possibilité d’aborder la question de la prédictibilité des marchés financiers sous un nouvel angle. Sans prétendre apporter une réponse définitive au débat entre les partisans de l’efficience des marchés et les chercheurs en finance comportementale, cette thèse vise à améliorer notre compréhension du processus de formation des prix sur les marchés financiers grâce à une approche Big Data. Plus précisément, cette thèse porte sur (1) la mesure du sentiment des investisseurs à fréquence intra-journalière, et le lien entre le sentiment des investisseurs et les rendements agrégés du marché,(2) la mesure de l’attention des investisseurs aux informations économiques et financières en temps réel, et la relation entre l’attention des investisseurs et la dynamique des prix des actions des sociétés à forte capitalisation, et enfin, (3) la détection des comportements suspicieux pouvant amoindrir le rôle informationnel des marchés financiers, et le lien entre le volume d’activité sur les réseaux sociaux et le prix des actions des entreprises de petite capitalisation. Le premier essai propose une méthodologie permettant de construire un nouvel indicateur du sentiment des investisseurs en analysant le contenu des messages publiés sur le réseau social Stock-Twits. En examinant les caractéristiques propres à chaque utilisateur (niveau d’expérience, approche d’investissement, période de détention), cet essai fournit des preuves empiriques montrant que le comportement des investisseurs naïfs, sujets à des périodes d’excès d’optimisme ou de pessimisme, a un impact sur la valorisation du marché action, et ce en accord avec les théories de la finance comportementale. Le deuxième essai propose une méthodologie permettant de mesurer l’attention des investisseurs aux informations en temps réel, en combinant les données des médias traditionnels avec le contenu des messages envoyés par une liste d’experts sur la plateforme Twitter. Cet essai démontre que lorsqu’une information attire l’attention des investisseurs, les mouvements de marchés sont caractérisés par une forte hausse des volumes échangés, une hausse de la volatilité et des sauts de prix. Cet essai démontre également qu’il n’y a pas de fuite d’information significative lorsque les sources d’informations sont combinées pour corriger un potentiel problème d’horodatage. Le troisième essai étudie le risque de manipulation informationnelle en examinant un nouveau jeu de données de messages publiés sur Twitter à propos des entreprises de petite capitalisation. Cet essai propose une nouvelle méthodologie permettant d’identifier les comportements anormaux de manière automatisée en analysant les interactions entre les utilisateurs. Étant donné le grand nombre de recommandations suspicieuses d’achat envoyées par certains groupes d’utilisateurs, l’analyse empirique et les conclusions de cet essai soulignent la nécessité d’un plus grand contrôle par les régulateurs de l’information publiée sur les réseaux sociaux ainsi que l’utilité d’une meilleure éducation des investisseurs individuels
The 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
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Books on the topic "Sentiment analytics"

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People, Sentiment and Social Network Analytics with Excel. Independently Published, 2019.

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Kumar, R. Analytical Approach-Sentimental Education. Lulu Press, Inc., 2010.

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Da Costa, Dia. Introduction. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040603.003.0001.

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This chapter introduces transnational feminist and affect theory frameworks, two activist troupes, and key concepts of sentimental capitalism and hunger called theater to argue the significance of analyzing a global discursive regime of creative economy policy within the same analytical frame as activist performance. Highlighting recent articulations, affects, and contradictions of Indian creative economy policy, it presents shifting discursive and political histories. Rather than focusing on capital-rich cultural production, it makes a case for attending to unrecognized creativity within activist performance whilst analyzing the latter’s messy collaborations with hegemonic regimes of creativity. Outlines the book’s organization: Part 1 historically and spatially locates a global discursive regime in India, Ahmedabad, and Delhi; Parts 2 and 3 are ethnographies of the two troupes.
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Book chapters on the topic "Sentiment analytics"

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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.

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Kagan, 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.

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Dominik, 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.

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Zadrozny, 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.

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Shi, 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.

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Garg, 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.

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Sarkar, 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.

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Anandarajan, 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.

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Anandarajan, 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.

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Anandarajan, 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.

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Conference papers on the topic "Sentiment analytics"

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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.

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Today's companies cannot overlook their reputation if they want to continue to survive. One way to measure that reputation is through two factors: sentiment analysis of news stories in the press about those companies and the financial data of those companies. In this research, the sentiment analysis of news stories about several Euro Stoxx 50 companies for the years 2016 and 2019 has been carried out. For this purpose, the lexicon-based tools VADER and Hu Liu have been used. Then the trends of the results obtained for this four-year period have been analyzed and compared with the trends in their operating results in the same time period. The results obtained indicate that there is a high correlation between the sentiments reflected in the news and their operating results, i.e., when news sentiment about a company improves, its reputation also improves, and this causes its sales to increase. The same is true in the opposite direction.
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Toivanen, 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.

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The rise of technology has enabled us to utilize even more integrated systems for social and health care, but these systems are often complex and time-consuming to learn for the end users without relevant training or experience. We aim to perform sentiment analysis based on the answers of eldercare workers that have taken a survey about the effects of digitalization in their work. The collecting of the panel survey data was done in two waves: in 2019 and 2021. For the sentiment analysis we compare these two waves to determine the effects of COVID-19 in the work of caretakers. The research questions we ask are the following: “Has technology affected care workers’ emotions in their work and how?" and “Has COVID-19 affected care workers’ views on digitalization in their work?”. The main results suggest that criticism of modern technology persists through time - that is, before and after the pandemic the same type of negative and positive sentiments are manifested in the results. However, there is an exception of COVID-related terminology that is only visible in the latter part (year 2021) of the analysis.
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Jeon, 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.

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Chen, 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.

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S, 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.

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Dabholkar, 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.

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Marcucci, 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.

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The aim of this work is to propose a strategy to classify texts (or parts of them) in an ordinal emotional scale to gauge a sentiment indicator in every domain. In particular, we develop a new dictionary for the Italian language which is built using an objective method where the polarities of synonyms and antonyms are accounted for in an iterative process. To build our sentiment indicator negations and intensifiers are also used, thus considering the context in which the single word is written. We apply our new dictionary to extract the sentiment from a set of around 40 issues of the Bank of Italy quarterly Economic Bulletin. Our results show that our strategy is able to correctly identify the sentiment expressed in the Bulletins, which is correlated to the main macroeconomic variables (such as national GDP, investment, consumption or unemployment rate). Our analysis shows that sentiment represents not only an evaluation of the stylistic way in which texts are written, but also a valid synthesis of all the external factors analysed in the same document.
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Dussoye, 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.

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Chen, 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.

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Anusha, 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.

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