Abstract: The application of sentiment analysis, also known as opinion mining, is more difficult in Chinese than in Indo-European languages, due to the compounding nature of Chinese words and phrases, and relatively lack of reliable resources in Chinese. This study used seed words, Chinese morphemes, which are mono-syllabic characters that function as individual words or be combined to create Chinese word and phrases, to classify movie reviews found on Yahoo. We use a lexicon based approach for discovering sentiments. Our lexicon is built from the Serendio taxonomy. The Serendio taxonomy consists of positive, negative, negation, stop words and phrases. A typical tweet contains word variations, emoticons, hashtags etc. We use preprocessing steps such as stemming, emotion detection and normalization, exaggerated word shortening and hashtag detection. After the preprocessing, the lexicon-based system classifies the tweets as positive or negative based on the contextual sentiment orientation of the words.
Keywords: sentiment analysis, opinion mining, morphemes, serendio taxonomy, exaggerated, lexicon based sys.