A Review of Sentiment Analysis Technology for Social Media Text and Its Value in Consumption Prediction

Abstract

With the rapid growth of social media, massive volumes of user-generated text data have accumulated on platforms. These unstructured expressions contain rich consumer sentiment information, which is of great significance for market research and judgment. This paper aims to clarify the technical workflow of sentiment analysis for social media text and to explore its value in consumption prediction. By sorting out the basic workflow of text preprocessing, sentiment scoring and topic identification, and by comparing the characteristics of three types of social media data sources, namely platform comments, short video danmaku and private message texts, this paper summarizes the performance of this technology in consumption prediction application scenarios such as purchase intention identification and commodity reputation evaluation. Meanwhile, this paper analyzes the impact of difficulties such as paid spam comments and ambiguous sentence recognition on the accuracy of this technology, and discusses the popularization status of low-cost sentiment analysis tools among small and medium-sized merchants. The research finds that although sentiment analysis technology can effectively mine consumption signals, social text noise and complex semantic problems still restrict prediction accuracy, and there is still large room for optimization of lightweight tools for typical merchants. In addition, in-depth exploration of multimodal data fusion and cross-platform sentiment analysis technology will become an important direction in the future. This study provides a theoretical reference and practical orientation for improving the application of social media text sentiment analysis technology in consumption prediction.