Fine-Grained Sentiment Analysis of User Comments on Educational Policies Based on Deep Learning
In the era of smart media,online social media platforms such as Weibo and Tiktok have become one of the most important channels to transmit information between the government and the public,and the public's comments on education policies on these platforms influence the implementation process,effect and subsequent policies.By integrating the LDA model and the LSTM model,and taking the"double reduction"policy as an example,the study mines the users'comments on education policies on online social medias and fine-grainedly analyzes the users'multidimensional subjective emotions towards education policies,so as to provide a reference for improving the implementation effect of education policies.It is found that the focus of online social media users'opinions on the"double reduction"policy is mainly concentrated on 16 comment objects under four themes,among which the users'emotions are positive in three aspects,including quality education,art activities,and academic qualifications;and the remaining 13 aspects are negative,such as out-of-school training,after-school service,education fairness,rich-poor gap,and employment.
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