A Novel Text Personalized Analysis Model Based on BP Neural Network

In online interactive platform, text analysis has greatly changed people's communication, thinking, and promoted the explosive growth of user-generated information. A large number of texts generated by users have become one of the most representative data sources of big data in recent years. Mining and analyzing user-generated information has become essential part on research of social development. The sentiment analysis on social media text as an information processing technology for analyzing, processing, summarizing and reasoning subjective texts with emotions has received extensive attention in academia and industry in recent years, and has been used among many areas of social media and many applications. The traditional text sentiment analysis research work mainly focuses on analyzing emotions from texts, but ignores the individualized differences of users in emotional expression, thus affecting the quality of analysis results. To solve the problems, this paper is about solving the problem of personalized sentiment analysis of social media texts. Considering the wide application of BP neural network technology in social media was proposed to solve the possible challenges of social media text personalized sentiment analysis