Relevant research data show that 69.8% of the 95s who use social networks for social activities tend to use various types of emoticons to express their emotional tendencies.The high frequency use of emoticons and their own distinctive emotional tendencies make emoticons an important corpus resource for text sentiment analysis.Based on this,a short text sentiment model EMME with multiple types of emoticons is proposed.The model integrates five types of emoticons into the text language for sentiment analysis in the Twitter corpus.Firstly,the CBOW model was used to construct the word vector,and then the convolution was used to fuse the concatenated word vectors.Then the MLP was used to realize the positive and negative sentiment classification of the text,and the linear regression was performed on the five types of emoticons and the text sentiment probability.The experimental results show that the MacroF1 value of EMME model is 14.81%,10.42% and 9.01% higher than that of MNB model,SVM model and EMB model respectively.The EMME model has achieved good classification results for different sample size.
关键词
情感分析/表情符号/深度学习/词向量/自然语言处理
Key words
Emotion analysis/Emoji/Deep learning/Word vector/Natural language processing
维普
万方数据