Contextual semantics for sentiment analysis of Twitter


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 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).
Uncontrolled Keywords: contextual semantics,sentiment analysis,Twitter,Media Technology,Information Systems,Computer Science Applications,Library and Information Sciences,Management Science and Operations Research
Publication ISSN: 0306-4573
Last Modified: 22 Jul 2024 07:05
Date Deposited: 04 Jun 2015 14:20
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-01
Published Online Date: 2015-03-07
Accepted Date: 2015-01-28
Authors: Saif, Hassan
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Fernández, Miriam
Alani, Harith

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