Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Sentiment analysis is an active research domain of the current era, thanks to its vast applications. The main objective is to classify the polarity of the given text as negative, positive, or neutral. Thus, researchers’ focus shifted towards the aspect or feature-based sentiment analysis because overall polarity does not determine the people’s views towards certain features. Therefore, Aspect-Based Sentiment Analysis (ABSA) helps us to identify the sentiments about various aspects of different services and products. However, their accurate identification and extraction are still challenging for the research community due to the complex nature of natural languages. This paper presents a method named Attention-based Multi-Channel Gated Recurrent Neural Network (Att-MC-GRU), which extracts aspects and analyzes textual reviews to predict or classify their sentiments. It introduces the hybrid approach by combining word embedding, part of speech (POS) tags, and contextual position information. The main novelty lies in proposal of a Multi-Channel Gated Recurrent Neural Network (MC-GRU), in contrast to the existing studies that consider Recurrent Neural Networks (RNN) comprising only a single input channel. In addition, word embedding, POS tags, and contextual position information collectively improve the identification and prediction accuracy of aspects and their associated sentiments. Due to the application of the filtering by the attention mechanism that figured out first the significant words, which helps to determine entities’ aspects related to the sentiment expressed. The empirical analysis proves the proposed approach’s effectiveness compared to the existing techniques in the relevant literature using standard datasets. According to the empirical analysis, the proposed model performs better in the F1-measure, with an overall achievement of 94% in the task of aspect extraction and 93% in the classification of sentiment.