Contextual semantics for sentiment analysis of Twitter. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Saif, Hassan; He, Yulan; Fernandez, Miriam; Alani, Harith
• Is Part Of: Information processing & management. Volume 52:Issue 1(2016:Jan.)
• Page Start: 5
• Page End: 19
• Publication Date: 2016-01
• Subjects: Sentiment analysis -- Contextual semantics -- Twitter; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.01.005
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We propose a semantic sentiment representation of words called SentiCircle. SentiCircle captures the contextual semantic of words from their co-occurrences. SentiCircle updates the sentiment of words based on their contextual semantics. SentiCircle can be used to perform entity- and tweet-level level sentiment analysis. 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.