Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2016

Abstract

Due to the huge volume and linguistic variation of data shared online, accurate detection of the sentiment of a message (polarity detection) can no longer rely on human assessors or through simple lexicon keyword matching. This paper presents a semi-supervised approach in constructing essential toolkits for analysing the polarity of a localised scarce-resource language, Singlish (Singaporean English). Corpus-based bootstrapping using a multilingual, multifaceted lexicon was applied to construct an annotated testing dataset, while unsupervised methods such as lexicon polarity detection, frequent item extraction through association rules and latent semantic analysis were used to identify the polarity of Singlish n-grams before human assessment was done to isolate misleading terms and remove concept ambiguity. The findings suggest that this multilingual approach outshines polarity analysis using only the English language. In addition, a hybrid combination of the Support Vector Machine and a proposed Singlish Polarity Detection algorithm, which incorporates unigram and n-gram Singlish sentic patterns with other multilingual polarity sentic patterns such as negation and adversative, is able to outperform other approaches in comparison. The promising results of a pooled testing dataset generated from the vast amount of unannotated Singlish data clearly show that our multilingual Singlish sentic pattern approach has the potential to be adopted in real-world polarity detection.

Keywords

Sentic computing, Polarity detection, Semi-supervised, Singlish, Twitter

Discipline

Computer Engineering | Digital Communications and Networking

Research Areas

Data Science and Engineering

Publication

Knowledge-Based Systems

Volume

105

First Page

236

Last Page

247

ISSN

0950-7051

Identifier

10.1016/j.knosys.2016.04.024

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.knosys.2016.04.024

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