Relevant bibliographies by topics / Sentiment Analysis Applications

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Sentiment Analysis Applications'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Sentiment Analysis Applications"

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Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.

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We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embedding’s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embedding’s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embedding’s in other NLP tasks.
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Anannya, Gupta, Deepali, Ahlawat Manvesh, Vedant, and Sharma Swati. "TWEEZER – Tweets Analysis." International Journal of Engineering and Management Research 10, no. 2 (2020): 111–15. https://doi.org/10.31033/ijemr.10.2.12.

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<strong>Twitter is&nbsp;one in all&nbsp;the foremost&nbsp;used applications by the people&nbsp;to precise&nbsp;their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of&nbsp;the general public. Twitter sentiment analysis is application for sentiment analysis&nbsp;of information&nbsp;which are extracted from the twitter(tweets). With&nbsp;the assistance&nbsp;of twitter people get opinion about several things&nbsp;round the&nbsp;nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from&nbsp;everywhere&nbsp;the globe.&nbsp;a decent&nbsp;sentimental analysis&nbsp;of information&nbsp;of this huge platform can&nbsp;result in&nbsp;achieve many new applications like &ndash; Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc.&nbsp;during this&nbsp;paper, we used two specific algorithm &ndash;Na&iuml;ve Bayes Classifier Algorithm for polarity Classification &amp; Hashtag classification for top modeling.&nbsp;this system&nbsp;individually has some limitations for Sentiment analysis. The goal of this report is&nbsp;to relinquish&nbsp;an introduction&nbsp;to the present&nbsp;fascinating problem and to present a framework&nbsp;which is able to&nbsp;perform sentiment analysis on online&nbsp;mobile&nbsp;reviews by associating modified na&iuml;ve bayes means algorithm with Na&iuml;ve bayes classification.</strong>
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Sunil Kumar, V., Vedashree C.R, and Sowmyashree S. "IMAGE SENTIMENTAL ANALYSIS: AN OVERVIEW." International Journal of Advanced Research 10, no. 03 (2022): 361–70. http://dx.doi.org/10.21474/ijar01/14398.

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Visual content, such as photographs and video, contains not only objects, locations, and events, but also emotional and sentimental clues. On social networking sites, images are the simplest way for people to communicate their emotions. Images and videos are increasingly being used by social media users to express their ideas and share their experiences. Sentiment analysis of such large-scale visual content can aid in better extracting user sentiments toward events or themes, such as those in image tweets, so that sentiment prediction from visual content can be used in conjunction with sentiment analysis of written content. Despite the fact that this topic is relatively new, a wide range of strategies for various data sources and challenges have been created, resulting in a substantial body of study. This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises. A description of new obstacles is also included, as well as an assessment of progress toward more sophisticated systems and related practical applications, as well as a summary of the studys findings.
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Tang, Duyu, Furu Wei, Bing Qin, Nan Yang, Ting Liu, and Ming Zhou. "Sentiment Embeddings with Applications to Sentiment Analysis." IEEE Transactions on Knowledge and Data Engineering 28, no. 2 (2016): 496–509. http://dx.doi.org/10.1109/tkde.2015.2489653.

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Mishra, Abhinav, Amit Ranjan, Arin Kumar, Vikash Sharma, and Projjwal Biswas. "Sentiment Analysis Using NLP." International Journal of Research 10, no. 11 (2023): 105–12. https://doi.org/10.5281/zenodo.10211177.

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<strong>Sentiment analysis, often referred to as opinion mining, is a vital subfield of Natural Language Processing (NLP) thatfocuses&nbsp;on&nbsp;understanding&nbsp;and&nbsp;classifying&nbsp;sentiments&nbsp;expressed&nbsp;in text data. This paper offers a comprehensive exploration of&nbsp;sentiment analysis, encompassing its methodologies, applications across various domains, and practical implications. We delve into&nbsp;the specifics of data collection, preprocessing, feature extraction, sentiment analysis techniques, and present empirical findings that&nbsp;highlight&nbsp;the effectiveness of our approach.</strong>
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H M, Dr Keerthi. "Review on Sentimental Analysis and Aspect Analysis with Codemix using LLM, BERT, and Naive Bayes." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40598.

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In the era of social media, where multilingual conversations are prevalent, analyzing code-mixed text poses unique challenges. This project presents a comparative analysis of sentiment analysis and aspect-based sentiment analysis on code-mixed data using advanced techniques like Large Language Models (LLM), BERT, and Naive Bayes. Sentiment analysis categorizes text into positive, negative, or neutral sentiments, while aspect-based analysis identifies opinions on specific topics, such as "price" or "quality" in reviews. By focusing on code-mixed text, this study compares the effectiveness of each method in understanding sentiments and specific opinions, paving the way for improved applications in multilingual settings.
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Mutmainah, Siti, Dhomas Hatta Fudholi, and Syarif Hidayat. "Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 312. http://dx.doi.org/10.30865/mib.v7i1.5486.

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The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.
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Prof., Richa Mehra, Saxena Diksha, and Gupta |. Joy Joseph Shubham. "Sentiment Analysis." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1370–73. https://doi.org/10.31142/ijtsrd23375.

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Sentiment Analysis SA is an ongoing field of research in text mining field. SA sentiment analysis is the computational treatment of opinions, sentiments and text. This s paper deals in a comprehensive overview of the recent updates in this field. Many recently proposed algorithms amend and various SA applications are investigated and presented briefly in this paper. The related fields to SA transfer learning, emotion detection, and building resources that attracted researchers recently are discussed. The main objective of this paper is to give nearly full image of SA techniques and the related fields with brief details. The main contributions in this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas. Prof. Richa Mehra | Diksha Saxena | Shubham Gupta | Joy Joseph &quot;Sentiment Analysis&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23375.pdf
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Zougris, Konstantinos. "Origins, Styles, and Applications of Text Analytics in Social Science Research." Encyclopedia 5, no. 2 (2025): 70. https://doi.org/10.3390/encyclopedia5020070.

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Textual analysis is grounded in conceptual schemes of traditional qualitative and quantitative content analysis techniques that have led to the hybridization of methodological styles widely used across social scientific fields. This paper delivers an extensive review of the origins and evolution of text analysis within the domains of traditional content analysis. Emphasis is given to the conceptual schemas and operational structure of latent semantic analysis, and its capacity to detect topical clusters of large corpora. Further, I describe the operations of Entity–Aspect Sentiment Analysis which are designed to measure and assess sentiments/opinions within specific contextual domains of textual data. Then, I conceptualize and elaborate on the potential of streamlining latent semantic and Entity–Aspect Sentiment Analysis complemented by Correspondence Analysis, generating an integrated operational scheme that would detect the topic structure, assess the contextual sentiment/opinion for each detected topic, test for statistical dependence of sentiments/opinions across topical domains, and graphically display conceptual maps of sentiments in topics space.
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SK, Syed Zabiulla, and Mausumi Goswami. "Sentiment Analysis Approaches and Applications –A Review." December 2023 5, no. 4 (2023): 381–98. http://dx.doi.org/10.36548/jucct.2023.4.004.

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With the advent of smartphones and the ease of access to the internet, people are mainly interested in sending textual messages through social media platforms. In many cases, customers would like to review the services provided by different providers in order to express satisfaction or dissatisfaction. The sentiments of users make a huge difference in the success of any business idea in the present digital age. As there are many competitors in every field of technology, health, and education, people would selectively want to use the resources that have positive opinions about them from the user community in the online reviews. There are different techniques to effectively estimate the user reviews, whether they are for or against a particular concept or the product. There are different techniques, like lexicon-based techniques, machine learning-based techniques, and deep learning-based techniques which are used to analyse the sentiments of the users’ reviews in order to improve user expectations. Lexicon-based techniques have many challenges, like the wrong interpretation of the meanings of the words and giving wrong sentiment scores to the words used by ignoring the grammatical constraints in the user reviews. There are many machine learning algorithms, like Logistic regression (LR), and Support Vector Machines (SVM) which can overcome the shortcomings of lexicon-based sentiment analysis models and could be used in various spheres of applications. The manuscript presents a detailed study in this regard.
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Dissertations / Theses on the topic "Sentiment Analysis Applications"

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Osika, Anton. "Statistical analysis of online linguistic sentiment measures with financial applications." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177106.

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Gavagai is a company that uses different methods to aggregate senti-ment towards specific topics from a large stream of real time published documents. Gavagai wants to find a procedure to decide which way of measuring sentiment (sentiment measure) towards a topic is most useful in a given context. This work discusses what criterion are desirable for aggregating sentiment and derives and evaluates procedures to select "optimal" sentiment measures. Three novel models for selecting a set of sentiment measures that describe independent attributes of the aggregated data are evaluated. The models can be summarized as: maximizing variance of the last principal compo-nent of the data, maximizing the differential entropy of the data and, in the special case of selecting an additional sentiment measure, maximizing the unexplained variance conditional on the previous sentiment measures. When exogenous time varying data considering a topic is available, the data can be used to select the sentiment measure that best explain the data. With this goal in mind, the hypothesis that sentiment data can be used to predict financial volatility and political poll data is tested. The null hypothesis can not be rejected. A framework for aggregating sentiment measures in a mathematically co-herent way is summarized in a road map.<br>Företaget Gavagai använder olika mått för att i realtid uppskatta sen-timent ifrån diverse strömmar av publika dokument. Gavagai vill hitta ett en procedur som bestämmer vilka mått som passar passar bäst i en given kontext. Det här arbetet diskuterar vilka kriterium som är önskvärda för att mäta sentiment samt härleder och utvärderar procedurer för att välja öptimalasentimentmått. Tre metoder för att välja ut en grupp av mått som beskriver oberoende polariseringar i text föreslås. Dessa bygger på att: välja mått där principal-komponentsanalys uppvisar hög dimensionalitet hos måtten, välja mått som maximerar total uppskattad differentialentropi, välja ett mått som har hög villkorlig varians givet andra polariseringar. Då exogen tidsvarierande data om ett ämne finns tillgängligt kan denna data användas för att beräkna vilka sentimentmått som bäst beskriver datan. För att undersöka potentialen i att välja sentimentmått på detta sätt testas hypoteserna att publika sentimentmått kan förutspå finansiell volatilitet samt politiska opinionsundersökningar. Nollhypotesen kan ej förkastas. En sammanfattning för att på ett genomgående matematiskt koherent sätt aggregera sentiment läggs fram tillsammans med rekommendationer för framtida efterforskningar.
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Clark, Eric Michael. "Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1006.

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Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. We mined data from several public Twitter endpoints to identify content relevant to healthcare providers and public health regulatory professionals. We began by compiling content related to electronic nicotine delivery systems (or e-cigarettes) as these had become popular alternatives to tobacco products. There was an apparent need to remove high frequency tweeting entities, called bots, that would spam messages, advertisements, and fabricate testimonials. Algorithms were constructed using natural language processing and machine learning to sift human responses from automated accounts with high degrees of accuracy. We found the average hyperlink per tweet, the average character dissimilarity between each individual's content, as well as the rate of introduction of unique words were valuable attributes in identifying automated accounts. We performed a 10-fold Cross Validation and measured performance of each set of tweet features, at various bin sizes, the best of which performed with 97% accuracy. These methods were used to isolate automated content related to the advertising of electronic cigarettes. A rich taxonomy of automated entities, including robots, cyborgs, and spammers, each with different measurable linguistic features were categorized. Electronic cigarette related posts were classified as automated or organic and content was investigated with a hedonometric sentiment analysis. The overwhelming majority (≈ 80%) were automated, many of which were commercial in nature. Others used false testimonials that were sent directly to individuals as a personalized form of targeted marketing. Many tweets advertised nicotine vaporizer fluid (or e-liquid) in various “kid-friendly” flavors including 'Fudge Brownie', 'Hot Chocolate', 'Circus Cotton Candy' along with every imaginable flavor of fruit, which were long ago banned for traditional tobacco products. Others offered free trials, as well as incentives to retweet and spread the post among their own network. Free prize giveaways were also hosted whose raffle tickets were issued for sharing their tweet. Due to the large youth presence on the public social media platform, this was evidence that the marketing of electronic cigarettes needed considerable regulation. Twitter has since officially banned all electronic cigarette advertising on their platform. Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indicators both during and post treatment. We have studied several active cancer patient populations, discussing their experiences with the disease as well as survivor-ship. We experimented with a Convolutional Neural Network (CNN) as well as logistic regression to classify tweets as patient related. This led to a sample of 845 breast cancer survivor accounts to study, over 16 months. We found positive sentiments regarding patient treatment, raising support, and spreading awareness. A large portion of negative sentiments were shared regarding political legislation that could result in loss of coverage of their healthcare. We refer to these online public testimonies as “Invisible Patient Reported Outcomes” (iPROs), because they carry relevant indicators, yet are difficult to capture by conventional means of self-reporting. Our methods can be readily applied interdisciplinary to obtain insights into a particular group of public opinions. Capturing iPROs and public sentiments from online communication can help inform healthcare professionals and regulators, leading to more connected and personalized treatment regimens. Social listening can provide valuable insights into public health surveillance strategies.
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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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Norell, Alexandra Jenny. "Application of sentiment analysis for information overload detection in an Ecommerce competitive environment." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42065.

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This master thesis is focusing on the information overload in digital marketing and using the method of sentiment analysis to detect if the issue occurs or not. A model and method of different sentiments (positive and negative) were organized, and evaluated based on the statistical and prominent findings of the emotional value in the customer satisfaction in online reviews. Findings were analyzed, as to what data, and categories showed value which proved information overload and these were thereafter connected to previous academic studies of sentiment analysis and customer satisfaction connected to information overload. The results of the analysis proved that the sentiment analysis had significance in some aspects and categories to combat the information overload issue in digital marketing for online consumers.
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Chouchani, Nadia. "Une approche de détection des communautés d'intérêt dans les réseaux sociaux : application à la génération d'IHM personnalisées." Thesis, Valenciennes, 2018. http://www.theses.fr/2018VALE0048/document.

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De nos jours, les Réseaux Sociaux sont omniprésents dans tous les aspects de la vie. Une fonctionnalité fondamentale de ces réseaux est la connexion entre les utilisateurs. Ces derniers sont engagés progressivement à contribuer en ajoutant leurs propres contenus. Donc, les Réseaux Sociaux intègrent également les créations des utilisateurs ; ce qui incite à revisiter les méthodes de leur analyse. Ce domaine a conduit désormais à de nombreux travaux de recherche ces dernières années. L’un des problèmes principaux est la détection des communautés. Les travaux de recherche présentés dans ce mémoire se positionnent dans les thématiques de l’analyse sémantique des Réseaux Sociaux et de la génération des applications interactives personnalisées. Cette thèse propose une approche pour la détection des communautés d’intérêt dans les Réseaux Sociaux. Cette approche modélise les données sociales sous forme d’un profil utilisateur social représenté par un ontologie. Elle met en oeuvre une méthode pour l’Analyse des Sentiments basées sur les phénomènes de l’influence sociale et d’Homophilie. Les communautés détectées sont exploitées dans la génération d’applications interactives personnalisées. Cette génération est basée sur une approche de type MDA, indépendante du domaine d’application. De surcroît, cet ouvrage fait état d’une évaluation de nos propositions sur des données issues de Réseaux Sociaux réels<br>Nowadays, Social Networks are ubiquitous in all aspects of life. A fundamental feature of these networks is the connection between users. These are gradually engaged to contribute by adding their own content. So Social Networks also integrate user creations ; which encourages researchers to revisit the methods of their analysis. This field has now led to a great deal of research in recent years. One of the main problems is the detection of communities. The research presented in this thesis is positioned in the themes of the semantic analysis of Social Networks and the generation of personalized interactive applications. This thesis proposes an approach for the detection of communities of interest in Social Networks. This approach models social data in the form of a social user profile represented by an ontology. It implements a method for the Sentiment Analysis based on the phenomena of social influence and homophily. The detected communities are exploited in the generation of personalized interactive applications. This generation is based on an approach of type MDA, independent of the application domain. In addition, this manuscript reports an evaluation of our proposals on data from Real Social Networks
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Tian, Nan. "Feature taxonomy learning from user generated content and application in review selection." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/101169/1/Nan_Tian_Thesis.pdf.

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This thesis developed new methods to find useful information from massive customer generated product review data in order to assist customers in decision making. It first examines the distinct features of review text to find useful information about the reviewed product using a number of existing techniques. Then, based upon derived product information, this thesis developed novel methods to assess the review quality in order to find most useful or helpful product reviews for customers.
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El, alaoui Imane. "Transformer les big social data en prévisions - méthodes et technologies : Application à l'analyse de sentiments." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0011/document.

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Extraire l'opinion publique en analysant les Big Social data a connu un essor considérable en raison de leur nature interactive, en temps réel. En effet, les données issues des réseaux sociaux sont étroitement liées à la vie personnelle que l’on peut utiliser pour accompagner les grands événements en suivant le comportement des personnes. C’est donc dans ce contexte que nous nous intéressons particulièrement aux méthodes d’analyse du Big data. La problématique qui se pose est que ces données sont tellement volumineuses et hétérogènes qu’elles en deviennent difficiles à gérer avec les outils classiques. Pour faire face aux défis du Big data, de nouveaux outils ont émergés. Cependant, il est souvent difficile de choisir la solution adéquate, car la vaste liste des outils disponibles change continuellement. Pour cela, nous avons fourni une étude comparative actualisée des différents outils utilisés pour extraire l'information stratégique du Big Data et les mapper aux différents besoins de traitement.La contribution principale de la thèse de doctorat est de proposer une approche d’analyse générique pour détecter de façon automatique des tendances d’opinion sur des sujets donnés à partir des réseaux sociaux. En effet, étant donné un très petit ensemble de hashtags annotés manuellement, l’approche proposée transfère l'information du sentiment connue des hashtags à des mots individuels. La ressource lexicale qui en résulte est un lexique de polarité à grande échelle dont l'efficacité est mesurée par rapport à différentes tâches de l’analyse de sentiment. La comparaison de notre méthode avec différents paradigmes dans la littérature confirme l'impact bénéfique de notre méthode dans la conception des systèmes d’analyse de sentiments très précis. En effet, notre modèle est capable d'atteindre une précision globale de 90,21%, dépassant largement les modèles de référence actuels sur l'analyse du sentiment des réseaux sociaux<br>Extracting public opinion by analyzing Big Social data has grown substantially due to its interactive nature, in real time. In fact, our actions on social media generate digital traces that are closely related to our personal lives and can be used to accompany major events by analysing peoples' behavior. It is in this context that we are particularly interested in Big Data analysis methods. The volume of these daily-generated traces increases exponentially creating massive loads of information, known as big data. Such important volume of information cannot be stored nor dealt with using the conventional tools, and so new tools have emerged to help us cope with the big data challenges. For this, the aim of the first part of this manuscript is to go through the pros and cons of these tools, compare their respective performances and highlight some of its interrelated applications such as health, marketing and politics. Also, we introduce the general context of big data, Hadoop and its different distributions. We provide a comprehensive overview of big data tools and their related applications.The main contribution of this PHD thesis is to propose a generic analysis approach to automatically detect trends on given topics from big social data. Indeed, given a very small set of manually annotated hashtags, the proposed approach transfers information from hashtags known sentiments (positive or negative) to individual words. The resulting lexical resource is a large-scale lexicon of polarity whose efficiency is measured against different tasks of sentiment analysis. The comparison of our method with different paradigms in literature confirms the impact of our method to design accurate sentiment analysis systems. Indeed, our model reaches an overall accuracy of 90.21%, significantly exceeding the current models on social sentiment analysis
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Erik, Cambria. "Application of common sense computing for the development of a novel knowledge-based opinion mining engine." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/6497.

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The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience.
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Dermouche, Mohamed. "Modélisation conjointe des thématiques et des opinions : application à l'analyse des données textuelles issues du Web." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO22007/document.

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Cette thèse se situe à la confluence des domaines de "la modélisation de thématiques" (topic modeling) et l'"analyse d'opinions" (opinion mining). Le problème que nous traitons est la modélisation conjointe et dynamique des thématiques (sujets) et des opinions (prises de position) sur le Web et les médias sociaux. En effet, dans la littérature, ce problème est souvent décomposé en sous-tâches qui sont menées séparément. Ceci ne permet pas de prendre en compte les associations et les interactions entre les opinions et les thématiques sur lesquelles portent ces opinions (cibles). Dans cette thèse, nous nous intéressons à la modélisation conjointe et dynamique qui permet d'intégrer trois dimensions du texte (thématiques, opinions et temps). Afin d'y parvenir, nous adoptons une approche statistique, plus précisément, une approche basée sur les modèles de thématiques probabilistes (topic models). Nos principales contributions peuvent être résumées en deux points : 1. Le modèle TS (Topic-Sentiment model) : un nouveau modèle probabiliste qui permet une modélisation conjointe des thématiques et des opinions. Ce modèle permet de caractériser les distributions d'opinion relativement aux thématiques. L'objectif est d'estimer, à partir d'une collection de documents, dans quelles proportions d'opinion les thématiques sont traitées. 2. Le modèle TTS (Time-aware Topic-Sentiment model) : un nouveau modèle probabiliste pour caractériser l'évolution temporelle des thématiques et des opinions. En s'appuyant sur l'information temporelle (date de création de documents), le modèle TTS permet de caractériser l'évolution des thématiques et des opinions quantitativement, c'est-à-dire en terme de la variation du volume de données à travers le temps. Par ailleurs, nous apportons deux autres contributions : une nouvelle mesure pour évaluer et comparer les méthodes d'extraction de thématiques, ainsi qu'une nouvelle méthode hybride pour le classement d'opinions basée sur une combinaison de l'apprentissage automatique supervisé et la connaissance a priori. Toutes les méthodes proposées sont testées sur des données réelles en utilisant des évaluations adaptées<br>This work is located at the junction of two domains : topic modeling and sentiment analysis. The problem that we propose to tackle is the joint and dynamic modeling of topics (subjects) and sentiments (opinions) on the Web. In the literature, the task is usually divided into sub-tasks that are treated separately. The models that operate this way fail to capture the topic-sentiment interaction and association. In this work, we propose a joint modeling of topics and sentiments, by taking into account associations between them. We are also interested in the dynamics of topic-sentiment associations. To this end, we adopt a statistical approach based on the probabilistic topic models. Our main contributions can be summarized in two points : 1. TS (Topic-Sentiment model) : a new probabilistic topic model for the joint extraction of topics and sentiments. This model allows to characterize the extracted topics with distributions over the sentiment polarities. The goal is to discover the sentiment proportions specfic to each of theextracted topics. 2. TTS (Time-aware Topic-Sentiment model) : a new probabilistic model to caracterize the topic-sentiment dynamics. Relying on the document's time information, TTS allows to characterize the quantitative evolutionfor each of the extracted topic-sentiment pairs. We also present two other contributions : a new evaluation framework for measuring the performance of topic-extraction methods, and a new hybrid method for sentiment detection and classification from text. This method is based on combining supervised machine learning and prior knowledge. All of the proposed methods are tested on real-world data based on adapted evaluation frameworks
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Lawani, Abdelaziz. "THREE ESSAYS ON THE APPLICATION OF MACHINE LEARNING METHODS IN ECONOMICS." UKnowledge, 2018. https://uknowledge.uky.edu/agecon_etds/68.

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Over the last decades, economics as a field has experienced a profound transformation from theoretical work toward an emphasis on empirical research (Hamermesh, 2013). One common constraint of empirical studies is the access to data, the quality of the data and the time span it covers. In general, applied studies rely on surveys, administrative or private sector data. These data are limited and rarely have universal or near universal population coverage. The growth of the internet has made available a vast amount of digital information. These big digital data are generated through social networks, sensors, and online platforms. These data account for an increasing part of the economic activity yet for economists, the availability of these big data also raises many new challenges related to the techniques needed to collect, manage, and derive knowledge from them. The data are in general unstructured, complex, voluminous and the traditional software used for economic research are not always effective in dealing with these types of data. Machine learning is a branch of computer science that uses statistics to deal with big data. The objective of this dissertation is to reconcile machine learning and economics. It uses threes case studies to demonstrate how data freely available online can be harvested and used in economics. The dissertation uses web scraping to collect large volume of unstructured data online. It uses machine learning methods to derive information from the unstructured data and show how this information can be used to answer economic questions or address econometric issues. The first essay shows how machine learning can be used to derive sentiments from reviews and using the sentiments as a measure for quality it examines an old economic theory: Price competition in oligopolistic markets. The essay confirms the economic theory that agents compete for price. It also confirms that the quality measure derived from sentiment analysis of the reviews is a valid proxy for quality and influences price. The second essay uses a random forest algorithm to show that reviews can be harnessed to predict consumers’ preferences. The third essay shows how properties description can be used to address an old but still actual problem in hedonic pricing models: the Omitted Variable Bias. Using the Least Absolute Shrinkage and Selection Operator (LASSO) it shows that pricing errors in hedonic models can be reduced by including the description of the properties in the models.
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Books on the topic "Sentiment Analysis Applications"

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Sweta, Soni. Sentiment Analysis and its Application in Educational Data Mining. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2474-1.

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Kolya, Anup Kumar, Dipankar Das, Soham Sarkar, and Abhishek Basu. Computational Intelligence Applications for Text and Sentiment Data Analysis. Elsevier Science & Technology Books, 2022.

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Kolya, Anup Kumar, Dipankar Das, Soham Sarkar, and Abhishek Basu. Computational Intelligence Applications for Text and Sentiment Data Analysis. Elsevier Science & Technology, 2022.

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Hussain, Amir, Erik Cambria, and Ranjan Satapathy. Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications. Springer, 2019.

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Hussain, Amir, Erik Cambria, and Ranjan Satapathy. Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications. Springer, 2018.

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Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications. Elsevier Science & Technology Books, 2024.

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Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications. Elsevier Science & Technology, 2024.

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Sentiment Analysis and Its Application in Educational Data Mining. Springer, 2024.

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Roger, Mccormick, and Stears Chris. Part IV Regulatory and Other Developments in the UK 2010‒2016, 16 General Legal and Conduct Risk Implications of the Crises and Regulator-Led Redress. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198749271.003.0017.

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In the post-financial crisis era, conduct regulation has permeated every facet of an institution’s operations. Not only does the legal framework for the delivery of redress impact the legal risk profile of an activity, but the very ‘approach’ of the regulator exacerbates the legal risk inherent in that activity. This issue is of specific relevance in the context of mis-selling cases, as the regulator may direct (or otherwise sponsor or influence) the terms of redress and in ways that may not be perfectly aligned to a strict application of liability under English common law. This chapter first explores post-financial crisis litigation and criminal charges and the influence of government and regulators on the provision of redress. It then analyzes a legal brake on liability — specifically, the impact of contractual estoppel in mis-selling cases. It concludes with a review of the regulatory factors relevant to redress and the leveraging of post-crises sentiment to promote extra-regulatory schemes.
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Book chapters on the topic "Sentiment Analysis Applications"

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Dominik, Hofer. "Sentiment Analysis." In Data Science – Analytics and Applications. Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-19287-7_17.

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Peng, Hong, and Jun Wang. "Sentiment Analysis." In Computational Intelligence Methods and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5280-5_7.

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Zhang, Huaping, and Jianyun Shang. "Sentiment Analysis." In Natural Language Processing and Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9739-4_10.

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Sindhu, C., and G. Vadivu. "Sentiment Analysis." In Secure Data Management for Online Learning Applications. CRC Press, 2023. http://dx.doi.org/10.1201/9781003264538-13.

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Abinaya, N., V. S. Harikrishnan, S. Santhiya, A. Sesili, and N. V. Nithya Shree. "Multimodal Sentiment Analysis Applications in Healthcare." In Sentiment Analysis Unveiled. CRC Press, 2025. https://doi.org/10.1201/9781003504832-3.

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Almars, Abdulqader, Xue Li, Xin Zhao, Ibrahim A. Ibrahim, Weiwei Yuan, and Bohan Li. "Structured Sentiment Analysis." In Advanced Data Mining and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69179-4_49.

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Bhatia, Gresha, Chinmay Patil, Pranit Naik, and Aman Pingle. "Tweet-Based Sentiment Analyzer." In ICT Analysis and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0630-7_36.

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Li, Weiwei, Yuqiang Li, and Yan Wang. "Chinese Microblog Sentiment Analysis Based on Sentiment Features." In Web Technologies and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45817-5_30.

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Sorvisto, Dayne, Patrick Cloutier, Kevin Magnusson, Taufik Al-Sarraj, Kostya Dyskin, and Giri Berenstein. "Live Twitter Sentiment Analysis." In Applications of Data Management and Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95810-1_4.

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Pavitha, N., Pranav Ratnaparkhi, Azfar Uzair, Aashay More, Swetank Raj, and Prathamesh Yadav. "Explainable AI for Sentiment Analysis." In ICT with Intelligent Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3571-8_41.

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Conference papers on the topic "Sentiment Analysis Applications"

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Aydoğan, Elif Hanife, and Feyza Yildirim Okay. "Sentiment Analysis of Reviews for E-Commerce Applications." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773400.

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Bokde, Rohit, and Pragati Dongare. "Evolution of Wearable Healthcare Technology: Opportunities, Challenges and Applications." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933459.

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Javagal, Bhavya N., and Sonal Sharma. "A Comprehensive Survey on Clinical Models for AI-Powered Medical Applications." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933337.

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G, Sandra, Salaja Silas, and Elijah Blessing Rajsingh. "Applications and Approaches of Sentiment Analysis: A Current Review." In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2024. http://dx.doi.org/10.1109/raics61201.2024.10690021.

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Rayasam, Namitha, VV Sai Sridhar, NS Pushkar, et al. "Multimodal Sentiment Analysis for Interviews and Proctoring." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719163.

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Awachat, Arya, Ananya Dube, and Shivnath Chaudhri. "ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933273.

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Zaâbi, Chayma, and Imen Boukhris. "Fine-Grained Sentiment Analysis in Financial Market." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912531.

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Gorai, Joy, and Dilip Kumar Shaw. "Multi-Modal Sentiment Analysis of Product Reviews." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10827296.

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Yueyue, Jiao. "Key Applications and Challenges of Virtual Software Simulation Technology in Data Resource Searching Schemes." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933047.

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S. Chu, Jason, and Sindhu Ghanta. "Integrative Sentiment Analysis: Leveraging Audio, Visual, and Textual Data." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140211.

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Exploring the area of multimodal sentiment analysis, this paper addresses the growing significance of this field, driven by the exponential rise in multimodal data across platforms like YouTube. Traditional sentiment analysis, primarily focused on textual data, often overlooks the complexities and nuances of human emotions conveyed through audio and visual cues. Addressing this gap, our study explores a comprehensive approach that integrates data from text, audio, and images, applying state-of-the-art machine learning and deep learning techniques tailored to each modality. Our methodology is tested on the CMU-MOSEI dataset, a multimodal collection from YouTube, offering a diverse range of human sentiments. Our research highlights the limitations of conventional text-based sentiment analysis, especially in the context of the intricate expressions of sentiment that multimodal data encapsulates. By fusing audio and visual information with textual analysis, we aim to capture a more complete spectrum of human emotions. Our experimental results demonstrate notable improvements in precision, recall and accuracy for emotion prediction, validating the efficacy of our multimodal approach over single-modality methods. This study not only contributes to the ongoing advancements in sentiment analysis but also underscores the potential of multimodal approaches in providing more accurate and nuanced interpretations of human emotions.
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Reports on the topic "Sentiment Analysis Applications"

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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the corporate level to detect sentiment anomalies for specific corporations (idiosyncratic risks), while employing a wide range of network metrics to monitor systemic risks. In the realm of supervisory technology (SupTech), anomaly detection in sentiment analysis serves as a proactive tool for financial authorities. By continuously monitoring sentiment trends, SupTech applications can provide early warnings of potential financial distress or systemic risks.
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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-driven empathy, ethical and privacy considerations, and future trends in affective computing and integration with technologies like AR/VR and IoT. The potential business advantages of adopting Emotionally Intelligent AI for enhanced customer satisfaction and long-term relationship management are highlighted, emphasizing the balance between technology and the human touch. Keywords: Emotionally Intelligent AI, customer service, empathy, sentiment analysis, emotion recognition, reinforcement learning, affective computing, personalized interactions, ethical AI, data privacy, AR/VR, IoT, human-AI interaction, future trends, business impact.
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