Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

• Published in: Journal of information science Vol. 44; no. 4; pp. 491 - 511
• Main Authors: Khoo, Christopher SG, Johnkhan, Sathik Basha
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-08-2018; Bowker-Saur Ltd
• Subjects: Accuracy; Comparative analysis; Data mining; News; Product reviews; Sentiment analysis; Texts; sentiment lexicon; Sentiment analysis; sentiment categorisation
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551517703514

This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts.