Analysis of Sentiment Tendency Based on Major Public Health Events
Xi Hai Xie, Le Chen · 2022 4th International Conference on Natural Language Processing (ICNLP) · 2022
The outbreak of the COVID-19 has seriously affected the lives of the public. On the network platform, there has been a lot of controversy about this issue, which caused panic among the people. Accurate Sentiment propensity analysis of user statements on various platforms can better guide public opinion and avoid unnecessary panic. This paper classifies data based on the Naive Bayesian algorithm because the traditional Naive Bayesian algorithm does not consider that the feature weights of the same feature word in different classes are different when classifying. Under the strong hypothesis of independence between features, the same feature word has the same importance, which will reduce the accuracy of the classifier. Therefore, this paper uses an improved TF-IDF algorithm to weight it and performs classification experiments. The experimental results show that this model can achieve better performance in text classification.