Abstract:

[Objective/Significance] With the rapid rise of we-media, domestic and foreign social media platforms have become an important channel for the rapid dissemination of various news events, and also an important platform for netizens to express their views and obtain information. Accordingly, it has become a hot research issue to obtain information by analyzing the sentiment of remarks on hot issues. Effective sentiment analysis can quickly obtain vital information such as event trends, public opinions, and attitudes. [Methods/Processes] the data source of this paper is comments on overseas social media platforms; First of all, for the network text with informal, unstructured, and too many emoticons, this paper designs the method of preprocessing data, including regularization, language detection, traditional to simplified, word segmentation, etc. Then, using Pointwise Mutual Information(PMI) and SKEP model to sentiment analysis. Finally, studying the application of sentiment analysis results. [Results/Conclusions] The method in this paper solves the difficulty that the data of practical application lacks annotation data. The accuracy of the model is 3.17% higher than that of the ERNIE model. In addition, by predicting the emotional tendency of users’ speech, we can earn high-quality intelligence, including the changing trend of topics over time and the key users in negative speech communication, etc. And the results are applied to the actual combat system.

Key words: social media, PMI, SKEP, sentiment analysis, application