Natural Language Processing Modeling for Content Mining and Sentiment Analysis in Online Media

The problems of word sense ambiguity and complex sentiment expression recognition in social media text sentiment analysis need to be solved urgently. By designing an analysis system based on improved BiLSTM-Attention, combined with an expanded sentiment lexicon, an optimized attention mechanism and domain knowledge, the complex sentiment recognition capability is greatly enhanced. Experimental results show that the system achieves 91.2% classification accuracy on the standard test set, and 86.7% and 89.3% accuracy in complex sentiment expressions and domain-specific phrase recognition, respectively. The system has been validated in several real-world application scenarios and provides new ideas for social media sentiment analysis.