Sentiment analysis method of comment text based on word vector with sentiment information

Abstract

In order to solve the problem of low accuracy of sentiment classification caused by neglecting the sentiment information of words in distributed word representation method,an improved sentiment analysis method incorporating weighted word vectors of sentiment information was proposed.According to the exclusive domain sentiment dictionary,combined with the dictionary and semantic rules,the sentiment information is integrated into the TF-IDF algorithm,and the weighted word vector representation method is obtained by using word2vec model.The method is used to compare the collected comments of tourist attractions in Hebei Province with the control group.The results show that compared with the sentiment analysis method based on distributed word vector representation,the accuracy and recall rate of positive text are increased by 61% and 66%,and the F value reached 903%,the accuracy and recall rate of negative text are increased by 60% and 72%,and the F value reached 896% by using the improved method of sentiment analysis integrated with sentiment information weighted word vector.Therefore,the improved method of sentiment analysis integrated with sentiment information weighted word vector can effectively improve the accuracy of sentiment analysis of comment text,and provide valuable reference for users to obtain more accurate comments.

Keywords

• natural language processing; semantic rules; sentiment information; tf-idf; word2vec; weighted word vector; sentiment analysis