Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention
This provides an incremental improvement for sentiment analysis tasks, applicable in areas like public opinion monitoring and product recommendation.
The paper tackled sentiment analysis by proposing an LSTM-based model with multi-head attention and TF-IDF optimization, achieving an accuracy of 80.28% on a test set, which is about 12% higher than standard LSTM models.
This work proposes an LSTM-based sentiment classification model with multi-head attention mechanism and TF-IDF optimization. Through the integration of TF-IDF feature extraction and multi-head attention, the model significantly improves text sentiment analysis performance. Experimental results on public data sets demonstrate that the new method achieves substantial improvements in the most critical metrics like accuracy, recall, and F1-score compared to baseline models. Specifically, the model achieves an accuracy of 80.28% on the test set, which is improved by about 12% in comparison with standard LSTM models. Ablation experiments also support the necessity and necessity of all modules, in which the impact of multi-head attention is greatest to performance improvement. This research provides a proper approach to sentiment analysis, which can be utilized in public opinion monitoring, product recommendation, etc.