Search results for "Sentiment Analysis"

showing 10 items of 46 documents

Ontology-Guided Approach to Feature-Based Opinion Mining

2011

The boom of the Social Web has had a tremendous impact on a number of different research topics. In particular, the possibility to extract various kinds of added-value, informational elements from users' opinions has attracted researchers from the information retrieval and computational linguistics fields. However, current approaches to socalled opinion mining suffer from a series of drawbacks. In this paper we propose an innovative methodology for opinion mining that brings together traditional natural language processing techniques with sentimental analysis processes and Semantic Web technologies. The main goals of this methodology is to improve feature-based opinion mining by employing o…

Information retrievalComputer scienceFeature extractionSentiment analysisFeature (machine learning)Selection (linguistics)Computational linguisticsOntology (information science)Social webData scienceSemantic Web
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Exploring the challenges of remote work on Twitter users’ sentiments: From digital technology development to a post-pandemic era

2022

The boost in the use and development of technology, spurred by COVID-19 pandemic and its consequences, has sped up the adoption of new technologies and digital platforms in companies. Specifically, companies have been forced to change their organizational and work structures. In this context, the present study aims to identify the main opportunities and challenges for remote work through the use of digital technologies and platforms based on the analysis of user-generated content (UGC) in Twitter. Using computer-aided text analysis (CATA) and natural language processing (NLP), in this study, we conduct a sentiment analysis developed with Textblob, which works with machine learning. We then …

Marketingcomputer-aided text analysissentiment analysistopic modelingremote workingtwitterUNESCO::CIENCIAS ECONÓMICASUGC
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Overview of the Evalita 2014 SENTIment POLarity Classification Task

2014

International audience; English. The SENTIment POLarity Classification Task (SENTIPOLC), a new shared task in the Evalita evaluation campaign , focused on sentiment classification at the message level on Italian tweets. It included three subtasks: subjectivity classification, polarity classification, and irony detection. SENTIPOLC was the most participated Evalita task with a total of 35 submitted runs from 11 different teams. We present the datasets and the evaluation methodology, and discuss results and participating systems. Italiano. Descriviamo modalit a e risultati della campagna di valutazione di sistemi di sentiment analysis (SENTIment POLarity Classification Task), proposta per la …

Polarity (physics)Computer science02 engineering and technologycomputer.software_genreNLP[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Task (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]020204 information systems0202 electrical engineering electronic engineering information engineeringSentiment Analysis[SHS.LANGUE]Humanities and Social Sciences/LinguisticsEvaluationsentiment analysis; twitter; irony; NLPironybusiness.industrySentiment analysis[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-TT]Computer Science [cs]/Document and Text Processingtwitter020201 artificial intelligence & image processingArtificial intelligencebusinessIrony detectionSocial MediacomputerNatural language processing
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A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language

2017

The paper illustrates a system for the automatic classification of the sentiment orientation expressed into reviews written in Italian language. A proper stratification of linguistic resources is adopted in order to solve the lacking of an opinion lexicon specifically suited for the Italian language. Experiments show that the proposed system can be applied to a wide range of domains.

Sentiment analysis Text Classification of ReviewsSettore INF/01 - InformaticaComputer scienceOrientation (computer vision)business.industryMultitier architectureItalian languageSentiment analysis02 engineering and technologyLexiconcomputer.software_genreRange (mathematics)020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerNatural language processing
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Sabiedrības attieksmes modelēšana, izmantojot sentimenta analīzi

2017

Šī darba mērķis ir izveidot sentimenta analīzes risinājumu, kuru paredzēts izmantot informācijas ieguves sistēmas koncepta izstrādē. Sentimenta analīze tiks veikta sociālo tīklu ziņām. Darba izstrādes sākumā tika veikta esošo sentimenta analīzes risinājumu izpēte un to rezultātu salīdzināšana. Tālāk tika veikta publiski pieejamo treniņdatu korpusu ievākšana. Papildus iegūtajiem datiem, tika izveidots latviešu valodai paredzēts sentimenta analīzes treniņdatu korpuss. Korpusa izveidošanas procesā tika veikta informācijas ieguves sistēmas koncepta izveide. Pēc nepieciešamo treniņdatu savākšanas, tika veikta ilgās īstermiņa atmiņas rekurentā neirona tīkla izveidošana un optimizēšana. Darba rezu…

Sentiment analysisSentimenta analīzeArtificial neural networksDatorzinātneMākslīgie neironu tīkli
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Positionless aspect based sentiment analysis using attention mechanism.

2021

Abstract Aspect-based sentiment analysis (ABSA) aims at identifying fine-grained polarity of opinion associated with a given aspect word. Several existing articles demonstrated promising ABSA accuracy using positional embedding to show the relationship between an aspect word and its context. In most cases, the positional embedding depends on the distance between the aspect word and the remaining words in the context, known as the position index sequence. However, these techniques usually employ both complex preprocessing approaches with additional trainable positional embedding and complex architectures to obtain the state-of-the-art performance. In this paper, we simplify preprocessing by …

SequenceInformation Systems and ManagementComputer sciencebusiness.industrySentiment analysisContext (language use)02 engineering and technologycomputer.software_genreLexiconManagement Information SystemsIndex (publishing)Artificial Intelligence020204 information systems0202 electrical engineering electronic engineering information engineeringPreprocessorEmbedding020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550SoftwareWord (computer architecture)Natural language processing
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A Novel Approach for Supporting Italian Satire Detection Through Deep Learning

2021

Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processing, and social multimedia sentiment analysis. In particular, as satirical texts include figurative communication for expressing ideas/opinions concerning people, sentiment analysis systems may be negatively affected; therefore, satire should be adequately addressed to avoid such systems’ performance degradation. This paper tackles automatic satire…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaSarcasmComputer scienceNatural language processingmedia_common.quotation_subjectSentiment analysisSatire detectionDeep learningContext (language use)Literal and figurative languageLinguisticsNewspaperPoliticsExaggerationComputational linguisticsmedia_common
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A Study on Classification Methods Applied to Sentiment Analysis

2013

Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to t…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniText corpusNaive Bayes classifierComputer sciencebusiness.industrySentiment analysisTF-IDFSentiment Classificationcomputer.software_genreClass Association RulesDomain (software engineering)Naive Bayes classifierRandom indexingArtificial IntelligenceSelection (linguistics)One-class classificationArtificial intelligenceRandom Indexingbusinesstf–idfcomputerNatural language processing
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Towards a fuzzy-linguistic based social network sentiment-expression system

2015

Liking allows users of Social Networks, blogs and online magazines to express their support of posts and artifacts by a simple click. Such function is very popular but lacks semantic power, and some platforms have augmented it by allowing to choose a pictographic depiction corresponding to a feeling. What is gained in depth is lost in simplicity, and the wide acceptance liking has enjoyed did not carried to the sentiment version. We outline a sentiment-expression hybrid system based on textual analysis and linguistic fuzzy Markov chains overcoming the intrinsic limitations of liking without burdening the user with complex choices.

Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceSentiment analysisSettore M-FIL/02 - Logica E Filosofia Della Scienzacomputer.software_genresocial networks sentiment analysis linguis- tic fuzzy Markov chainsExpression (architecture)Fuzzy linguisticArtificial intelligencebusinesscomputerNatural language processing
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What drives the helpfulness of online reviews? A deep learning study of sentiment analysis, pictorial content and reviewer expertise for mature desti…

2021

Los destinos turísticos se ven cada vez más afectados por la información relacionada con los viajes que se comparte a través de las redes sociales. Basándose en teorías de proceso dual sobre cómo los individuos procesan la información, este estudio examina el papel de las rutas de procesamiento de información central y periférica en la formación de las percepciones de los consumidores sobre la utilidad de las reseñas en línea de destinos maduros. Llevamos a cabo un proceso de dos pasos para abordar la utilidad percibida del contenido generado por el usuario, un análisis de sentimiento utilizando técnicas avanzadas de aprendizaje automático (aprendizaje profundo) y un análisis de regresión. …

Strategy and Managementmedia_common.quotation_subjectDestinations:CIENCIAS ECONÓMICAS [UNESCO]perceived helpfulnessPerceptionVoting0502 economics and businessSocial mediaBusiness and International Managementmedia_commonMarketing05 social sciencesSentiment analysisInformation processingdeep learningUNESCO::CIENCIAS ECONÓMICASAdvertisingRegression analysisdual-processing theorysentiment analysisTourism Leisure and Hospitality ManagementHelpfulness050211 marketingmature destinationsPsychology050212 sport leisure & tourismuser-generated contentJournal of Destination Marketing & Management
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