New energy market competition incentive, how to improve the product accurately, so as to attract consumers, will become the key point for enterprises to occupy the market. From the perspective of improving products, this paper constructs a review information dimension mapping theme emotion analysis model. Firstly, LDA model is used to extract the topic of comment information. Secondly, topic mapping is carried out on the comment information to get the correlation degree between each comment information and each dimension topic. Finally, the LSTM model is constructed for sentiment analysis, and the emotional intensity of consumers in each dimension theme is measured.
The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.
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