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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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Abstract:
Part 1: Computational Intelligence for Text Analysis ; International audience ; Model-based approaches for context-sensitive contrastive summarization depend on hand-crafted features for producing a summary. Deriving these hand-crafted features using machine learning algorithms is computationally expensive. This paper presents a deep learning approach to provide an end-to-end solution for context-sensitive contrastive summarization. A hierarchical attention model referred to as Contextual Sentiment LSTM (CSLSTM) is proposed to automatically learn the representations of context, feature and opinion words present in review documents of each entity. The resultant document context vector is a high-level representation of the document. It is used as a feature for context-sensitive classification and summarization. Given a set of summaries from positive class and a negative class of two entities, the summaries which have high contrastive score are identified and presented as context-sensitive contrastive summaries. Experimental results on restaurant dataset show that the proposed model achieves better performance than the baseline models.
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Keyword:
[INFO]Computer Science [cs]; Attention model; Context-aware sentiment classification; Opinion summarization; Pointer-generator network
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URL: https://hal.inria.fr/hal-03434805/document https://hal.inria.fr/hal-03434805 https://doi.org/10.1007/978-3-030-63467-4_2 https://hal.inria.fr/hal-03434805/file/507484_1_En_2_Chapter.pdf
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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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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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