Sentiment Lexicon Generation for an Under-Resourced Language

Sentiment analysis and opinion mining are actively explored nowadays. One of the most important resources for the sentiment analysis task is sentiment lexicon. This paper presents our study in building domain-specific sentiment lexicon for Indonesian language. Our main contributions are (1) methods to expand sentiment lexicon using sentiment patterns and (2) a technique to classify the polarity of a word using the sentiment score. Our method is able to generate sentiment lexicon automatically by using a small seed of sentiment words, user reviews, and part-ofspeech (POS) tagger. We develop the lexicon for Indonesian language using a set of seed words translated from English sentiment lexicon and expand them using sentiment patterns found in the user reviews. Our results show that the proposed method can generate additional lexicon with sentiment accuracy of 77.7%.