Research On Sentiment Analysis Method Of Chinese Weibo Users

The research goal in the field of text Sentiment Analysis is to analyze the commendatory or derogatory sentiment,opinion information,etc.expressed or implied by people for entities and their attributes from the text.It can also be called Opinion Mining.It is a sub-field of Natural Language Processing(NLP),and its research tasks include:subjectivity analysis,tendency analysis,comment mining,etc.Weibo connects the relationship between users,enables users to share,disseminate,and obtain relevant information in real time,and provides people with a convenient and efficient way to communicate on the Internet.As a result,massive amounts of text data are generated every day.At the same time,thanks to the Weibo platform features such as low threshold,freedom of speech,etc.,malicious comments emerge in endlessly,causing psychological harm to users.Analyzing the user's sentimental tendency from the user's Weibo plays a vital role in grasping the user's sentiment changes in real time to pay attention to it.At present,in the research of sentiment analysis,the analysis technology for English text is becoming more and more mature.Due to the characteristics of Chinese rhetoric,the existing technology is not suitable for Chinese analysis.And when the user's sentiment polarity of current and historical is known,in order to prevent users who are psychologically harmed from making extreme behaviors,the extent to which future sentiments will be affected by historical sentiments also needs to be predicted.Therefore,in response to the above two issues,this paper explores and studies an effective sentiment calculation model for Chinese analysis that uses the BERT pre-training model,and practices a method of user sentiment prediction that uses the Hawkes process function.Based on the above tasks,the judgment and prediction of the sentiment tendency of Chinese Weibo users have been achieved.The main research contents are as follows:(1)BERT pre-training model: The architectural theory of this model is pre-training and fine-tuning.The pre-training stage is to use a deep neural network model to learn complex text semantic information,that is,for the input text,initialize the word vector and make it positional encoded,so that the model recognizes word order features and relation of dependencies.Then uses the self-attention mechanism module to obtain multiple meaning expressions of Chinese characters,and introduces residual linking and normalization to make the calculation more efficient,and finally activates it with an activation function.The fine-tuning stage is used for downstream tasks in a transfer learning manner to achieve better results with a smaller and a bit of parameters.(2)Hawkes process function: a self-exciting point process that can establish a method of connecting one's past and present,that is,events that have occurred in the past will increase the probability of new events in the future.Predict the polarity and intensity of new events based on the time and sentimental intensity of an event that has occurred,thereby predicting the user's sentimental tendency.Based on the above research contents,build a deep learning environment and collect users' Chinese Weibo corpus for experiments.The experimental results show that the BERT model used in this paper improves the accuracy of Chinese Weibo sentiment calculation.The method based on the Hawkes process function can achieve the prediction of the user's sentimental tendency.