The Technological Development and Application Frontiers of Social Media Sentiment Analysis: A Ten Year Review (2016-2026)

Social media sentiment analysis technology has been rapidly iterative in the past decade, but the existing research lacks a systematic review of its technology evolution and application context. This paper reviews the relevant literature from 2016 to 2026 from two dimensions of technology paradigm and application scenarios. The technical level has experienced four paradigm iterations from the explicit emotion classification of SVM and TextCNN, to the implicit emotion detection led by BiLSTM+Attention and BERT, to the multimodal fusion model, and to the generative reasoning of the big language model. The application level has been deeply embedded in public opinion governance, brand insight, financial forecasting and public health monitoring. The research points out that the lack of cross domain generalization, short text noise interference and model bias are the core bottlenecks. In the future, people should build a cross language unified model and a lightweight multimodal framework, and establish a fairness evaluation system.