Minimally-Supervised Sentiment Lexicon Induction Model: A Case Study of Malay Sentiment Analysis

Abstract

Vital to the task of mining sentiment from text is a sentiment lexicon, or a dictionary of terms annotated for their a priori information across the semantic dimension of sentiment. Each term has assigned a general, out-of-context sentiment polarity. Unfortunately, online dictionaries and similar lexical resources do not readily include information on the sentiment properties of their entries. Moreover, manually compiling sentiment lexicons is tedious in terms of annotator time and effort. This has resulted in the emergence of a large volume of research concentrated on automated sentiment lexicon generation algorithms. Most of these algorithms were designed for English, attributable to the abundance of readily available lexical resources in this language. This is not the case for low-resource languages such as the Malay language. Although there has been an exponential increase in research on Malay sentiment analysis over the past few years, the subtask of sentiment lexicon induction for this particular language remains under-investigated. We present a minimally-supervised sentiment lexicon induction model specifically designed for the Malay language. It takes as input only two initial paradigm positive and negative terms, and mines WordNet Bahasa’s synonym chains and Kamus Dewan’s gloss information to extract subjective, sentiment-laden terms. The model automatically bootstraps a reliable, high coverage sentiment lexicon that can be employed in Malay sentiment analysis on full-text. Intrinsic evaluation of the model against a manually annotated test set demonstrates that its ability to assign sentiment properties to terms is on par with human judgement.