Sentiment Analysis of Comment Data Based on BERT-ETextCNN-ELSTM

Abstract

With the rapid popularity and continuous development of social networks, users’ communication and interaction through platforms such as microblogs and forums have become more and more frequent. The comment data on these platforms reflect users’ opinions and sentiment tendencies, and sentiment analysis of comment data has become one of the hot spots and difficulties in current research. In this paper, we propose a BERT-ETextCNN-ELSTM (Bidirectional Encoder Representations from Transformers–Enhanced Convolution Neural Networks–Enhanced Long Short-Term Memory) model for sentiment analysis. The model takes text after word embedding and BERT encoder processing and feeds it to an optimized CNN layer for convolutional operations in order to extract local features of the text. The features from the CNN layer are then fed into the LSTM layer for time-series modeling to capture long-term dependencies in the text. The experimental results proved that compared with TextCNN (Convolution Neural Networks), LSTM (Long Short-Term Memory), TextCNN-LSTM (Convolution Neural Networks–Long Short-Term Memory), and BiLSTM-ATT (Bidirectional Long Short-Term Memory Network–Attention), the model proposed in this paper was more effective in sentiment analysis. In the experimental data, the model reached a maximum of 0.89, 0.88, and 0.86 in terms of accuracy, F1 value, and macro-average F1 value, respectively, on both datasets, proving that the model proposed in this paper was more effective in sentiment analysis of comment data. The proposed model achieved better performance in the review sentiment analysis task and significantly outperformed the other comparable models.