A hybrid approach to identifying sentiment polarity for new words

Microblog is a typical form of heterogeneous information. For this information, identifying sentiment polarity of new words plays a fundamental role in sentiment analysis. In this paper, we proposed a hybrid approach using both statistic and syntax information to identifying the sentiment polarity of new words. We first filter the raw tweets out some noises and segment the clean data with POS tagging. Next, we collect new words by filtering rules. Then, we assign each new word with a polarity using both statistics and patterns information. We evaluate our approach on a real dataset from Sina Weibo, achieving a relatively high F-score of 0.241 compared with the baseline of 0.22.