An Approach of Sentiment-Topic Mining Based on User Online Comments on New Energy Vehicles
DOI:
https://doi.org/10.15837/ijccc.2025.6.6934Keywords:
New energy vehicles, Online comments, Sentiment analysis, Topic miningAbstract
The automobile industry is undergoing revolutionary changes. New energy vehicles, instead of fuel vehicles, gradually occupy the automobile sales market and have become an irresistible trend. With the development of social networks and automobile forums, consumers are increasingly using Internet channels to post online comments on automobile products or services after purchasing automobile products or enjoying services. These online comments contain a large number of user opinions and sentiment information and reflect the description of products or services and user experience from the user perspective. To discover potential user demands and sentiment orientations, this paper proposed a sentiment-topic mining model based on online comments on new energy vehicles. Taking the Pacific Automobile website as an example, we used Python to collect 46,530 online comments, analyzed positive and negative user sentiment orientations based on the Word2Vec-SVM model, analyzed the causes of negative comments based on the LDA model, and discovered potential user demands and product inadequacies. The research results revealed that users were satisfied with "appearance", "power", "configuration" and "handling" of new energy vehicles, while they are dissatisfied with "space", "interior", "energy consumption" (battery life) and "comfort". The causes of user dissatisfaction in each evaluation dimension were different, reflecting different user demands and current inadequacies of new energy vehicles. This sentiment-topic mining model can be generalized to discover potential user demands and analyze user sentiments in various industries, help enterprises gain insight into market trends, guide product improvement, and enhance product competitiveness.
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