Kai Schirmer


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Sentiment Analysis for Issues Monitoring Using Linguistic Resources
Ecaterina Rascu | Kai Schirmer | Johann Haller
Actes de la 12ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Sentiment analysis dealing with the identification and evaluation of opinions towards a topic, a company, or a product is an essential task within media analysis. It is used to study trends, determine the level of customer satisfaction, or warn immediately when unfavourable trends risk damaging the image of a company. In this paper we present an issues monitoring system which, besides text categorization, also performs an extensive sentiment analysis of online news and newsgroup postings. Input texts undergo a morpho-syntactic analysis, are indexed using a thesaurus and are categorized into user-specific classes. During sentiment analysis, sentiment expressions are identified and subsequently associated with the established topics. After presenting the various components of the system and the linguistic resources used, we describe in detail SentA, its sentiment analysis component, and evaluate its performance.