Expansion of Sentiment Lexicon Based on Label Propagation

Sentiment lexicons play a vital role in the field of sentiment classification. The existing sentiment lexicon has problems such as limited coverage and poor adaptability in the field. Building an sentiment lexicon with large coverage and strong adaptability in the field has become a challenge in this field. This paper proposes a new method for selecting seed words. Firstly, the seed words are manually selected based on the general lexicon. Then, the word vectors are trained on the corpus by the seed words selected artificially. Finally, the expanded seed words are obtained. Obtaining sentiment polarity by calculating the similarity between the seed words and the candidate sentiment words, constructing propagation map and propagation matrix to construct sentiment lexicon. The experimental results show that the method can obtain higher accuracy and better robustness compared with the baseline method.