Sentiment analysis of online public opinion based on CNN-BiLSTM and attention mechanism

Abstract

Emotion recognition from social network texts aims to mine netizens’ subjective emotions, such as stances and emotional tendencies, over an event, which is imperative for monitoring Internet public opinion. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are widely used in the field of text classification. The combination of the two can exploit the CNNs’ feature extraction ability and BiLSTM’s ability to extract context dependency. However, for emotional recognition, it is also necessary to consider some specific words in online social text. Therefore, we constructed a model for emotion recognition based on Internet public opinion in three steps. First, the CNN was used to extract local features of social network texts. Second, context-related global features were extracted using the BiLSTM. Finally, we introduced an attention mechanism to obtain important features. Experiments were conducted using netizens’ comments from a microblog during the COVID-19 epidemic as the dataset. Experimental results showed that the feature vector of the proposed model (i.e., the CNN-BiLSTM-Attention model) contains richer semantic information of texts, which can effectively improve the performance of emotion recognition from Internet public opinions.