E-Commerce Platform User Evaluation Sentiment Analysis and Emotion-Oriented Marketing Strategy Research Based on Big Data Technology

This study aims to optimize the sentiment analysis process of user evaluation texts through big data technology and natural language processing technology. This includes developing or improving existing emotion classification algorithms (such as ISOMAP algorithm) to more accurately identify positive, negative, and neutral emotions in user evaluations. Based on sentiment analysis, deeply explore user evaluations with different emotional tendencies and extract key factors that affect user emotions. These key factors may involve multiple aspects such as product quality, service attitude, and logistics speed, which can help companies gain a more comprehensive understanding of user needs and pain points. First, this paper summarizes the theoretical basis and research status of sentiment analysis and clarifies the background and significance of the research. In this study, natural language processing technology and the ISOMAP algorithm were used to classify the emotional tendency of user evaluation texts and three emotional categories were identified: positive, negative and neutral. Then, big data analysis tools are used to dig deeply into user evaluations with different emotional tendencies and extract the key factors affecting user emotions. On this basis, this paper constructs an emotionally-oriented marketing strategy framework. Through the case analysis, the application value of sentiment analysis of e-commerce and the effectiveness of marketing strategy are verified. The research results show that the application of big data technology in user evaluation sentiment analysis can not only provide enterprises with accurate market insights but also guide enterprises to develop more effective marketing strategies and enhance their market competitiveness.