Abstract
With the rise of Amazon, Netflix, and other e-commerce portals, many users widely depend on reviews by customers who used the product, before deciding to go ahead with a purchase. Users’ reviews are generally diverse. While some reviews can genuinely be relied upon, a few other reviews, at the same time, can be misleading. In this paper, an improved recommendation system with aspect-based sentiment analysis that replaces the attention sublayers with simple fast Fourier transform in the input embedding, to model heterogeneous semantic relationships in text is proposed. Developing a high-quality recommendation system, to recommend with excellent coverage over different aspects of a product review, is the need of the hour these days. Different deep learning techniques for aspect-based recommendation systems make use of attention mechanism to capture diverse syntactic and semantic relationships from the reviews. Experimental analysis on datasets such as SemEval 2014 Laptop Reviews, Restaurant Reviews, Twitter Data shows that the aspect-based sentiment analysis of the model outperforms the baseline models considerably, with an accuracy rate of 75.06 %, 79.93%, and 72.31% on Laptop Reviews, Restaurant Reviews, and Twitter data, respectively. Despite using attention-based model with many parameters, the model is able to be trained with less number of parameters with the proposed variant of the recommendation systems with aspect-based sentiment analysis model. The performance of the model was also evaluated on three fine-tuned environments showing promising results.
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Safar, S., Jose, B.R., Santhanakrishnan, T. (2022). An Improved Recommendation System with Aspect-Based Sentiment Analysis. In: Mathew, J., Santhosh Kumar, G., P., D., Jose, J.M. (eds) Responsible Data Science. Lecture Notes in Electrical Engineering, vol 940. Springer, Singapore. https://doi.org/10.1007/978-981-19-4453-6_5
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