A Method of Sentiment Analysis and Visualized Interaction Based on Ernie-Tiny and BiGRU

Abstract

Sentiment analysis of subjective texts in social media is beneficial to help people adjust and intervene in a negative mental state in time, which is significant to mental health care. At present, limited by the accuracy of word segmentation, sentiment analysis of subjective text has difficulties in dealing with context, sentence patterns, and word co-occurrence. This paper aims to propose an efficient method of semantic feature representation and sentiment analysis, thereby providing a basis for sentiment visualization and interactive applications. Based on Ernie-Tiny and BiGRU, this paper proposes a sentiment analysis model ET_s_BG+p to solve problems in analyzing Chinese subjective texts’ complex semantics, diverse sentence patterns, and shortness. The model inputs the semantic features obtained via Ernie-Tiny into BiGRU and then splices the output with the sentence vectors of Ernie-Tiny to form final text features and perform sentiment classification. Experiments are performed on a dataset integrating text comments from Weibo, takeaway, and e-commerce platforms. The results show that the model proposed in this paper performs best in most of the evaluation indicators compared with baseline models such as CNN, BiLSTM, and GRU. The experiments show that the accuracy of the model on the dataset built in this research is 84.30%, the precision is 83.95%, the recall rate is 88.35%, and the F1 value is 85.98%. At the same time, based on ET_s_BG+p, this paper develops a prototype visual display platform that integrates functions such as text input, sentiment analysis, and agent interaction, which can provide support for daily emotion monitoring and adjustment.