Emotional Analysis of Cigarette Consumers Based on CNN and BiLSTM Deep Learning Model

Abstract

Cigarette online reviews can truly reflect the word-of-mouth of cigarettes, and help cigarette industrial and commercial enterprises to understand consumers’ cigarette use experience and cigarette word-of-mouth dynamics. In order to extract effective consumer experience information from massive online reviews of cigarette consumption, this paper studies the text sentiment analysis of cigarette online reviews. This paper presents a feature fusion model of convolutional neural network and BiLSTM. Experimental results show that the proposed feature fusion model effectively improves the accuracy of text classification. The model can provide new insight for the evaluation of cigarette management, dynamically monitor the change of consumers’ emotion, and grasp the trend of consumers’ emotion in the tobacco market environment in time.