Research on Multi-personality Microblog Sentiment Classification Based on BiLSTM + Self-Attention

As one of the most popular social network platforms, microblog is an important way for people to express their views and feelings. Psychological research shows that personality influences the way people express their feelings. In view of the problem that personality is rarely considered in sentiment classification of microblogs, this paper proposes a microblog sentiment classification model, P-BiLSTM-SA, based on BiLSTM + self-attention and combining personality factors. According to "Big Five" theory, the model will first group the microblog texts into different personality groups based on users’ personality. Then, the BiLSTM model and the self-attention mechanism are combined to train the basic classifiers of each group. Finally, the ensemble learning method is used to fuse the basic classifiers and output the final affective labels. In order to verify the effectiveness of self-attention and personality in sentiment classification, two groups of comparative experiments were conducted. The results of the first group of experiments show that,based on the comprehensive average performance of the four evaluation indicators of accuracy, precision, recall rate and F₁, the P-BiLSTM-SA proposed in this paper improved 0.036, 0.017 and 0.025, compared with the model P-LSTM, P-BiLSTM and BiLSTM-SA. It shows that the self-attention mechanism can effectively learn the key information of the text. The results of the second group of experiments show that compared with the BiLSTM-SA without personality factors, the accuracy, precision, recall and F₁ of the proposed model P-BiLSTM-SA is improved by 0.012 on average, indicating that the combination of personality factors is useful for sentiment classification.