结合金融领域情感词典和注意力机制的细粒度情感分析

To address the lack of sufficient annotated corpus and the poor performance of common sentiment analysis models for the task of entity-level sentiment analysis of financial texts. This paper builds a multi-million level corpus of sentiment analysis of financial domain entities and labels more than five thousand financial domain sentiment words as financial domain sentiment dictionary. Based on this financial domain dataset, we propose an Attention-based Recurrent Network Combined with Financial Lexicon, called FinLexNet. FinLexNet model uses LSTM to extract category-level information based on financial domain sentiment dictionary and another LSTM to extract semantic information at the word-level, which can effectively obtain information about the characteristics of financial domain words. In addition, in order to get more attention to the financial sentiment words, an attention mechanism based on the financial domain sentiment dictionary is proposed. Finally, experiments were conducted on the dataset we constructed, which shows that our model has achieved better performance than the comparative models.