Building Domain-Specific Sentiment Lexicon by Sentiment Seed Expansion

Sentiment words extraction is one of the most important subtask of sentiment analysis. However, most sentiment analysis tasks are based on emotion words extracted manually, which requires a lot of manual intervention. In order to overcome this problem, this paper presents a framework to automatically expand the domain-specific sentiment lexicon by sentiment seeds extraction from a large domain corpus of user comments in the automotive field. We use word2vec to learn word embedding and label a small amount of dataset as positive or negative data as training dataset. We extract domain-specific sentiment seeds embedding from training dataset by calculating the sentiment score of the words in training dataset. Afterwards, these sentiment seeds are expanded to cast a large-scale automotive-specific sentiment lexicon by synonyms and k-means algorithm, without any manual annotation. Experimental results show that our approach is able to obtain a large number of new domain-specific sentiment words in the automotive field, and our lexicon reveals better performance than universal sentiment lexicon on user comments sentiment classification.