Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Abstract

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not b e suitab le for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these prob lems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can b e applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts usi ng Naïve Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naïve Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The b est sentiment cla ssification performance using proposed feature expansion is ob tained b y accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large numb er of them.

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