Chinese Implicit Sentiment Analysis Based on Hierarchical Knowledge Enhancement and Multi-Pooling

Sentiment analysis is one of the significant application fields of natural language processing. Researchers divide the sentiment into explicit sentiment and implicit sentiment according to whether the expression contains sentiment words or not. Most of the existing research focuses on explicit sentiment tasks. However, implicit sentiment analysis is difficult to study because of its implicit tendency to express sentiment and the problem of “weak features” and “multi-confounding weak features”. We develop a novel model based on “Hierarchical Knowledge Enhancement and Multi-pooling”(HKEM), which fully integrates the knowledge information of different levels in the text through hierarchical knowledge enhancement to alleviate the “weak feature” problem. At the same time, the multi-pooling method is used to extract and analyze multiple features, and the “multi-confounding weak features” are modeled. In this paper, comparative experiments have been conducted on the SMP-ECISA2019 dataset, and the results show that the accuracy of this model is comparable to that of the current best model, and the F1 score is 5.9% higher than that of the best model, indicating that the model proposed in this paper can accurately analyze Chinese implicit sentiment and achieve the state-of-the-art in the comprehensive performance evaluation, which proves the effectiveness and superiority of the model in this paper.