Attention based convolutional residual squeeze excited capsule network for aspect based sentiment classification in Malayalam movie reviews

Abstract

One of the main functions of Natural Language Processing (NLP) is sentiment analysis, which extracts attitudes, ideas, views or judgments about a given topic. The Internet is a vast and unstructured information source full of text documents, including evaluations and opinions. Firstly, the input texts are pre-processed using an efficient NLP method such as tokenization, stemming, removal of empty sets, stop words removal and morphological segmentation. These pre-processed texts serve as the input for the feature extraction stage. Using the three methods of Improved Term Frequency-Inverse Document Frequency (ITF-IDF), Latent Semantic Analysis (LSA) and Extended Bidirectional Encoder Representations from Transformers (E-BERT), the review-based features are extracted. Aspect-based features are extracted from the review text using the Aspect Related Feature (ARF) extraction method. By enhancing term weights with improved frequency scaling, the model improves on regular TF-IDF and includes more subtle contextual meanings and relationships with words. Finally, applying both types of features, a new Attention-based Convolutional Residual Squeeze Excited Capsule Network (A-CR-SECapNet) model is created to classify sentiment polarities as positive, negative and neutral. The Convolutional Residual Module captures spatial relationships to learn deeper networks that mitigate vanishing gradients. The SE Module improves the attentiveness of the network by dynamically reweighting the channel-wise information from features that correlate with important sentiment variables. The CapNet preserves the spatial relationships between words to maintain the dependence of sentiment between features. Finally, the performance of the model is further improved by fine-tuning the parameters using the Modified Gazelle Optimization (MGO) optimization method. In the results section, the proposed model is compared to the existing model in terms of precision, f1-score, accuracy, recall, mean absolute error (MSE) and mean absolute percentage error (MAPE). The proposed model produced the best results, demonstrating its superiority.