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EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
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Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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MULTI-STRATEGY SENTIMENT ANALYSIS OF CONSUMER REVIEWS WITH PARTIAL PHRASE MATCHING ...
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MULTI-STRATEGY SENTIMENT ANALYSIS OF CONSUMER REVIEWS WITH PARTIAL PHRASE MATCHING ...
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Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis
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A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique
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In: Information ; Volume 12 ; Issue 9 (2021)
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LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
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In: Applied Sciences ; Volume 11 ; Issue 23 (2021)
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Meta-Learner for Amharic Sentiment Classification
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In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models
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In: Electronics ; Volume 10 ; Issue 21 (2021)
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A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation
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In: Computer Sciences & Mathematics Forum; Volume 2; Issue 1; Pages: 14 (2021)
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A Multilingual Lexicon-based Approach for Sentiment Analysis in Social and Cultural Information System Data
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Context Aware Contrastive Opinion Summarization
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In: IFIP Advances in Information and Communication Technology ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS) ; https://hal.inria.fr/hal-03434805 ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.16-29, ⟨10.1007/978-3-030-63467-4_2⟩ (2020)
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Модели и методы анализа тональности в текстах на башкирском языке ... : Models and methods for sentiment analysis of texts in Bashkir language ...
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ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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Mining social media as a measure of equity market sentiment
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Influence des lexiques d'émotions et de sentiments sur l'analyse des sentiments Application à des critiques de livres
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In: CORIA-EARIA 2019 ; https://hal.archives-ouvertes.fr/hal-02448203 ; CORIA-EARIA 2019, Mar 2019, Villeurbanne, France (2019)
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Abstract:
International audience ; Consumers are used to consulting posted reviews on the Internet before buying a product. But it’s difficult to know the global opinion considering the important number of those reviews. Sentiment analysis afford detecting polarity (positive, negative, neutral) in a expressed opinion and therefore classifying those reviews. Our purpose is to determine the influence of emotions on the polarity of books reviews. We define “bag-of-words” representation models of reviews which use a lexicon containing emotional (anticipation, sadness, fear, anger, joy, surprise, trust, disgust) and sentimental (positive, negative) words. This lexicon afford measuring felt emotions types by readers. The implemented supervised learning used is a Random Forest type. The application concerns Amazon platform’s reviews. ; Les consommateurs ont l'habitude de consulter les critiques postées sur internet avant d'acheter un produit. Mais, il est difficile pour le consommateur de connaître l'opinion globale du produit vu le nombre important de ces critiques. L'analyse des sentiments permet de détecter la polarité (positive, négative ou neutre) sur une opinion exprimée et donc de classer ces critiques. Notre but est de déterminer l'influence de l'expression des émotions sur l'analyse de la polarité des critiques de livres. Nous définissons des modèles de représentation "sac de mots" de critiques qui s'appuient sur un lexique contenant des mots porteurs de sentiments (positif, négatif) et d'émotions (anticipation, tristesse, peur, colère, joie, surprise, confiance, dégoût). Ce lexique permet de mesurer les types d'émotions ressenties par les lecteurs. L'apprentissage supervisé mis en oeuvre est de type forêt aléatoire (Random Forest). L'application concerne des critiques de la plateforme Amazon.
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Keyword:
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO]Computer Science [cs]; Analyse d'émotions (texte); Analyse de sentiments; Classification de polarité de sentiments; Classification of sentiments polarity; Emotion analysis (text); Sentiment analysis
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URL: https://hal.archives-ouvertes.fr/hal-02448203
https://hal.archives-ouvertes.fr/hal-02448203/file/RJCRI_2019_paper_9.pdf
https://hal.archives-ouvertes.fr/hal-02448203/document
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FEEL: a French Expanded Emotion Lexicon
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In: https://hal-lirmm.ccsd.cnrs.fr/lirmm-02136090 ; 2019, ⟨swh:1:dir:35a446bb6f0808aa0db6f5bcd032f43e9ec71591;origin=https://hal.archives-ouvertes.fr/lirmm-02136090;visit=swh:1:snp:9d258bfd8a07f79cb8063b6ac33d6e710bd3e3f3;anchor=swh:1:rev:2266d773cf993b8718a1d7ca1d1bce145118f527;path=/⟩ (2019)
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Overwhelmed by Negative Emotions? Maybe You Are Being Cyber-bullied!
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In: Symposium On Applied Computing ; SAC 2019 - The 34th ACM/SIGAPP Symposium On Applied Computing ; https://hal.archives-ouvertes.fr/hal-02020829 ; SAC 2019 - The 34th ACM/SIGAPP Symposium On Applied Computing, Apr 2019, Limassol, Cyprus. ⟨10.1145/3297280.3297573⟩ (2019)
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