Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series

In today’s information age, the development of hot events is timely and rapid under the influence of the powerful Internet. Online social media, such as Weibo in China, has played an important role in the process of spreading public opinions and events. Sentiment analysis of social network texts can effectively reflect the development and changes of public opinions. At the same time, prediction and judgment of public opinion development can also play a key role in assisting decision-making and effective management. Therefore, sentiment analysis for hot events in online social media texts and judgment of public opinion development have become popular topics in recent years. At present, research on textual sentiment analysis is mainly aimed at a single text, and there is little-integrated analysis of multi-user and multi-document in unit time for time series. Moreover, most of the existing methods are focused on the information mined from the text itself, while the feature of identity differences and time sequence of different users and texts on social platforms are rarely studied. Hence, this paper works on the public opinion texts about some specific events on social network platforms and combines the textual information with sentiment time series to achieve multi-document sentiment prediction. Considering the related features of different social user identities and time series, we propose and implement an effective time+user dual attention mechanism model to analyze and predict the textual information of public opinion. The effectiveness of the proposed model is then verified through experiments on real data from a popular Chinese microblog platform called Sina Weibo.