earticle

영어

With the rapid development of the Internet, the number of Internet users has increased significantly. More and more viewers tend to publish their opinions and comments on movies in blogs, forums, Weibo, and online videos. This kind of large-scale spontaneous online movie commentary, by its unique diversity and universality, has become an important reference factor for studying the popularity of movies in the public and evaluating the pros and cons of movies. However, the user comment data of e-commerce platforms such as Weibo is often huge and vulnerable to the socio-economic environment, showing a large scale, dynamic and complex. How to analyze users' interests and preferences from massive and text-type comments, extract topics of interest to users, and satisfy and dissatisfied goods and their attributes, and become a new era of e-commerce enterprises to improve the quality of goods and services, grasp the social prevalence Trends, as well as fundamental, critical issues that must be addressed and addressed by precision marketers. Based on this, I decided to use statistical methods to empirically analyze the characteristics of the review data and conduct data mining, and use neural network learning, artificial intelligence and other techniques to analyze user sentiment and user preferences/interests in the review data. The user sentiment analysis and user preference/interest mining in the comment data are targeted for the two consumer behavior analysis tasks, and the user's interest network will be extracted to make the recommendation system of interest to the users.