A fine-grained deep learning model using embedded-CNN with BiLSTM for exploiting product sentiments

Abstract

As technology advances, Facebook, Twitter, and microblogging sites have become the most effective platforms for communication and information exchange. Through these forums, people can share their views and experiences. These platforms enable discussion about a certain product that can be a valuable resource used to inform any decision-making process. For such studies, the majority of advanced-level researchers employed deep learning and machine learning models in conjunction with natural language processing (NLP). In recent years, the use of pre-trained models, such as Glove and BERT, in aspect-based sentiment analysis (ABSA) has increased. In ABSA, the auxiliary information is required to distinguish each aspect of this fine-grained task. However, BERT’s input format is restricted to a collection of words that cannot include more context knowledge. To address this problem, a BiLSTM and embedded CNN-based deep learning model has been presented for sentiment analysis at the aspect level. Initially, datasets were compiled from several sources. Then, an auxiliary feature was extracted using standard NLP. The auxiliary features were further refined and transformed into feature vectors based on the proposed embedded CNN model. Finally, a BiLSTM-based classification has been performed for sentiment classification. The experimental evaluation demonstrated that the performance of the suggested technique achieves on the SemEval dataset in terms of F1 score and accuracy by 81.7 and 83.3 percentage points, respectively, and on the product review dataset by 80.8 and 83.1 percentage points, respectively.