Aspect-Based Sentiment Analysis using the Attentional Encoder Network

Natural language processing is growing significantly and has gained much attention with the advent of the World Wide Web and search engines, and researchers have witnessed an explosion of information in different languages. Sentiment analysis is one of the most active fields of study in natural language processing that focuses on text classification and is used to identify, extract and analyze subjective information from text sources. Aspect-based sentiment analysis is a text analysis technique that classifies comments by aspect and identifies the sentiment associated with each aspect. This analysis can be used to automatically analyze the feedback of customers' comments to different parts of goods or services and help employers to focus on points that need quality improvement. In this paper, we will introduce a new architecture based on deep learning for aspect-based sentiment analysis. This architecture will use an attention-encoder network-based model with multiple multi-head attention and a pointwise convolutional transform (which is a parallelizable and interactive alternative to LSTM and is applied to compute hidden states of input embeddings). Testing this architecture on three different datasets, including restaurants and laptops, SemEval 2014 Task 4 and  ACL 14 Twitter dataset, in all three datasets, the polarity of emotions is positive, neutral and negative, which is compared with modern methods of sentiment analysis. Based on the aspect, it will show the high accuracy of this method. For example, the aspect-based sentiment analysis on the restaurant dataset has shown 0.8187% accuracy, which has increased the accuracy by 1.97% compared to modern methods.