Saif, Hassan, He, Yulan, Fernandez, Miriam and Alani, Harith (2016) Contextual semantics for sentiment analysis of Twitter. Information Processing & Management, 52 (1). pp. 5-19. doi:10.1016/j.ipm.2015.01.005

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Abstract

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.

Item Type:	Journal Article
Subjects:	H Social Sciences > HM Sociology P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Twitter (Firm), Semantics, Internet users -- Attitudes
Journal or Publication Title:	Information Processing & Management
Publisher:	Elsevier
ISSN:	0306-4573
Official Date:	January 2016
Dates:	Date Event January 2016 Published 7 March 2015 Available 28 January 2015 Accepted
Volume:	52
Number:	1
Page Range:	pp. 5-19
DOI:	10.1016/j.ipm.2015.01.005
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 611242 European Research Council http://viaf.org/viaf/130022607 611242 Seventh Framework Programme http://dx.doi.org/10.13039/100011102 GJHZ20120613110641217 Shenzhen University http://dx.doi.org/10.13039/501100009019

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