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A Mixed Approach of Expanding Sentiment Lexicon Based on word2vec and Phrase Dependency Tree

Abstract— As one of the basic and crucial tasks in sentiment analysis, sentiment lexicon generation has great significance. Due to diversities of sentiment words, automatic expansion of sentiment lexicon has become a challenging research topic in recent years. This paper proposes a new approach to expand sentiment lexicon. At first, the approach selects some sentiment seed words, then uses word2vec to train word embeddings and find out the words which have potential similarities with seed words to generate a candidate sentiment lexicon, and then finds out the words which have conjunctive relations with seed words based on the phrase dependency tree to generate another candidate sentiment lexicon; Finally, we take the words appearing in the two candidate sentiment lexicons as final expanded sentiment lexicon. Experimental results demonstrate the effectiveness of our approach and the advantages against state of-the-art baselines.

Index Terms— sentiment analysis, sentiment lexicon, word2vec, phrase dependency tree

Junxia Wang, Pu Zhang, Yinghao Wang
Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, CHINA

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Cite: Junxia Wang, Pu Zhang, Yinghao Wang, "A Mixed Approach of Expanding Sentiment Lexicon Based on word2vec and Phrase Dependency Tree," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 410-416, Beijing, 25-27 June, 2017.