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A Multi-Layer Dual Attention Deep Learning Model With Refined Word Embeddings for Aspect-Based Sentiment Analysis

Although the sentiment analysis domain has been deeply studied in the last few years, the analysis of social media content is still a challenging task due to the exponential growth of multimedia content. Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify. Aspect-based sentiment analysis creates a need to develop explicit extraction techniques using syntactic parsers to exploit the relationship between the aspect and sentiment terms. Along with the extraction approaches, word embeddings are generated through Word2Vec models for the continuous low-dimensional vector representation of text that fails to capture the significant sentiment information. This paper presents a co-extraction model with refined word embeddings to exploit the dependency structures without using syntactic parsers. For this purpose, a deep learning-based multilayer dual-attention model is proposed to exploit the indirect relation between the aspect and opinion terms. In addition, word embeddings are refined by providing distinct vector representations to dissimilar sentiments, unlike the Word2Vec model. For this, we have employed a sentiment refinement technique for pre-trained word embedding model to overcome the problem of similar vector representations of opposite sentiments. Performance of the proposed model is evaluated on three benchmark datasets of SemEval Challenge 2014 and 2015. The experimental results indicate the effectiveness of our model compared to the existing state-of-the-art models for the aspect-based sentiment analysis.