A Collaborative CNN-LSTM Framework with Attention Mechanism for Aspect-Based Sentiment Analysis
Israr Ur Rehman, Zulfiqar Ali, Zahoor Jan · 2024
Aspect-based sentiment analysis (ABSA) seeks to extract fine-grained sentiment information by locating sentiments connected to particular elements in a text. Traditional sentiment analysis techniques often fail to address the intricacies of aspect-based sentiments due to their inability to capture complex con-textual dependencies. The suggested approach makes use of long short-term memory networks (LSTMs) to describe sequential dependencies and convolutional neural networks (CNNs) for effective feature extraction. Simultaneously, the attention mech-anism improves interpretability and performance by focusing on pertinent portions of the input text. The CNN component extracts local features from word embeddings, which the LSTM then processes to capture long-term dependencies. By giving important words a larger weight, the attention mechanism further refines the result, ensuring that the most informative parts of the text are emphasized during sentiment and aspect prediction. Extensive experiments, including Automobile, Movie, and hotel reviews, use benchmark datasets to demonstrate the efficacy of our model. The collaborative framework significantly outper-forms baseline models, improving F1 scores, recall, accuracy, and precision.