Contextual semantics for sentiment analysis of Twitter

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Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.



Original languageEnglish
Pages (from-to)5-19
Number of pages19
JournalInformation Processing and Management
Issue number1
Early online date7 Mar 2015
Publication statusPublished - Jan 2016

Bibliographic note

© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Funding: EU-FP7 project SENSE4US (Grant No. 611242); Shenzhen International Cooperation Research Funding (Grant No. GJHZ20120613110641217).


  • contextual semantics, sentiment analysis, Twitter

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