Research on Sentiment Analysis of Short Video Network Public Opinion by Integrating BERT Multi-level Features

Abstract

The era of self-media and the widespread popularity of online social software have led to short video platforms becoming “incubators” easily for the origin and fermentation of public opinion events. Analyzing the public opinion comments on these platforms is crucial for the early warning, handling, and guidance of such incidents. In view of this, this paper proposes a text classification model combining BERT and TextCNN, named BERT-MLFF-TextCNN, which integrates multi-level features from BERT for sentiment analysis of relevant comment data on the Douyin short video platform. Firstly, the BERT pre-trained model is used to encode the input text. Secondly, semantic feature vectors from each encoding layer are extracted and fused. Subsequently, a self-attention mechanism is integrated to highlight key features, thereby effectively utilizing them. Finally, the resulting feature sequence is input into the TextCNN model for classification. The results demonstrate that the BERT-MLFF-TextCNN model outperforms BERT-TextCNN, GloVe-TextCNN, and Word2vec-TextCNN models, achieving an [F1] score of 0.977. This model effectively identifies the emotional tendencies in public opinions on short video platforms. Based on this, using the TextRank algorithm for topic mining allows for the visualization of thematic words related to the sentiment polarity of public opinion comments, providing a decision-making reference for relevant departments in the public opinion management work.

Keywords

• network public opinion; sentiment analysis; theme visualization; bert