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Improving Convolutional Network using K-max mechanism for Sentiment Analysis Tasks

Sentiment analysis is a process in which natural language processing uses a computer to automatically comprehend specified topics from written or spoken language. In this paper, by comparing the experiments of recursive neural network and convolutional neural network on the English film review data set, and then adding attention mechanism, K-max mechanism, and highway network mechanism to the two models respectively, comparing the results of the models, we found The original convolutional neural network lags far behind the recursive neural network in the sentiment analysis task for English movie reviews, but it adds an optimized K-max mechanism and attention to the convolutional neural network and the recursive neural network respectively. After the mechanism, through analysis, the results show that among several optimization mechanisms, the K-max mechanism has a great optimization effect on the performance of the convolutional neural network in the field of English movie critic sentiment analysis tasks.