Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis
Identifieur interne : 004958 ( Hal/Curation ); précédent : 004957; suivant : 004959Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis
Auteurs : Alexandre Denis [France] ; Samuel Cruz-Lara [France] ; Nadia Bellalem [France] ; Lotfi Bellalem [France]Source :
Abstract
This paper describes the Synalp-Empathic system that competed in SemEval-2014 Task 9B Sentiment Analysis in Twitter. Our system combines syntactic-based va-lence shifting rules with a supervised learning algorithm (Sequential Minimal Optimization). We present the system, its features and evaluate their impact. We show that both the valence shifting mech-anism and the supervised model enable to reach good results.
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<front><div type="abstract" xml:lang="en">This paper describes the Synalp-Empathic system that competed in SemEval-2014 Task 9B Sentiment Analysis in Twitter. Our system combines syntactic-based va-lence shifting rules with a supervised learning algorithm (Sequential Minimal Optimization). We present the system, its features and evaluate their impact. We show that both the valence shifting mech-anism and the supervised model enable to reach good results.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis</title>
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<abstract xml:lang="en">This paper describes the Synalp-Empathic system that competed in SemEval-2014 Task 9B Sentiment Analysis in Twitter. Our system combines syntactic-based va-lence shifting rules with a supervised learning algorithm (Sequential Minimal Optimization). We present the system, its features and evaluate their impact. We show that both the valence shifting mech-anism and the supervised model enable to reach good results.</abstract>
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