SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

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Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure.

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Publication date31 Dec 2014
Publication titleThe semantic web: trends and challenges : 11th international conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, proceedings
EditorsValentina Presutti, Claudia d’Amato, Fabien Gandon, Mathieu d’Aquin, et al
Place of PublicationChem (CH)
Number of pages16
ISBN (Electronic)978-3-319-07443-6
ISBN (Print)978-3-319-07442-9
Original languageEnglish
Event11th international Conference on Semantic Web: trends and challenges - Anissaras, Crete, Greece
Duration: 25 May 201429 May 2014

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th international Conference on Semantic Web: trends and challenges
Abbreviated titleESWC 2014
CityAnissaras, Crete


  • semantics, sentiment analysis, Twitter

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