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

Preview

PDF WRAP-contextual-semantics-sentiment-analysis-Twitter-He-2018.pdf - Accepted Version - Requires a PDF viewer. Download (966Kb) | Preview

Request Changes to record.

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
Date of first compliant deposit:	29 September 2018
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

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads