A Detailed Sentiment Analysis Of Weibo Texts

Nowadays,it is becoming more and more common for people to share information and communicate through online social platforms.The emotional information contained in Weibo reflects the social behavior and emotional polarity tendency of China,which has become one of the hot topics in Natural Language Processing.In view of this,this paper analyzes the feelings expressed by users in the field of Weibo based on Weibo data.The research contents of this paper are as follows:In the study based on sentiment dictionary,there are still a large number of new words that have not been entered into sentiment dictionary.The dictionaries do not contain comprehensive emotional polar words in Weibo.On the basis of the Weibo basic dictionary established in this article,an extended sentiment dictionary is built.At the same time,the expansion of the dictionary is based on the SOPMI algorithm,and the domain sentiment dictionary dedicated to the field of Weibo is constructed.A weight algorithm combining emotion words with specific semantic rules is designed.By putting forward the feature mining algorithm based on classifier,the best classifier is obtained through experiments on feature selection and combination,feature dimension setting and classification algorithm selection,so as to obtain the best accuracy rate of detailed emotional classification.It is difficult to spend a lot of time to extract features manually now.In this paper,a hybrid model algorithm combining word vector with depth learning is proposed to train data content.Firstly,Word2 vec neural network language model is used to generate word vectors instead of manually extracted feature information,and then the emotional information contained in the word vectors is extracted in the LChybrid model to complete the detailed polar classification of emotions.Compared with the above methods and the traditional deep learning algorithms,the validity and accuracy of the proposed model in the Weibo text sentiment analysis task are proved.