A Novel Sentiment Polarity Detection Framework for Chinese

Nowadays, mining opinions or sentiment from online user-generated text has become a research hot spot. Although a large amount of lexicon-based Chinese polarity detection works have been done, the existing methods have one common flaw: that even the same word can have opposite polarities among different seed lexicons. This is known as polarity fuzziness. To enhance the performance of Chinese sentiment polarity detection, we start from a two-aspect lexicon expansion so that the polarity fuzziness can be avoided. Specifically, we detect sentiment polarity for new words and revise sentiment polarity for words already defined in seed lexicons. Then, we formulate a novel sentiment polarity detection framework for Chinese (SPDFC) with more attention to fine-grained sentiment processing, which is involved in symmetrical mapping, sentiment feature pruning and text representation. In this way, words' polarity can be directly taken as features, penetrating further in the polarity detection phase. According to our experimental results, the proposed SPDFC framework can achieve the best overall performance from the perspective of Chinese polarity detection, sentiment feature pruning, and text representation compared to other classical and state-of-the-art methods.

Automated summary

This research focuses on sentiment polarity detection from online user-generated text. The existing lexicon-based methods suffer from polarity fuzziness, where the same word can have opposite polarities in different seed lexicons. To address this issue, the study proposes a two-aspect lexicon expansion approach to enhance Chinese sentiment polarity detection. By detecting and revising sentiment polarity for new and existing words in seed lexicons and incorporating fine-grained sentiment processing through symmetrical mapping, sentiment feature pruning, and text representation, the proposed framework achieves the best overall performance compared to other methods.