A ConvLSTM model with word-level attention for sentiment analysis of review data

Deep learning models hold promise for sentiment analysis (SA), traditional CNN and LSTM methods are limited in capturing both local and temporal features, affecting accuracy. This paper introduces a ConvLSTM model with word-level attention to address these issues. By combining CNNs and LSTMs with an attention mechanism, the model improves sentiment classification by focusing on relevant features. Experiments on the IMDB, SD4A, and Sentimen140 datasets show that the proposed model outperforms baseline methods in precision.