Attention-based position-aware framework for aspect-based opinion mining using bidirectional long short-term memory

Aspect-based Opinion Mining is a form of fine-grained Sentiment Analysis and it models the semasiolog-ical relationship between aspect terms and context words in a sentence. The presence of a variety of con -text words has a significant impact on a sentence's sentiment polarity. As a result, while designing a model, it is necessary to consider the interaction of aspects and context words. Although existing approaches have taken into account an aspect's position in a sentence, much of the research works have not explored the use of Sentiment Lexicons with the Deep Learning algorithms. In this paper, we propose a framework for an Attention-based position-aware Bidirectional Long Short-Term Memory network for Aspect-based Opinion Mining that incorporates a Sentiment Intensity Lexicon. The aspect word's pre-trained vector is adjusted to be closer to semantically and sentimentally similar nearest neighbors and further away from sentimentally dissimilar neighbors. The proposed framework calculates aspect weights by concatenating the external knowledge in the form of lexicon sentiment intensity scores with word embeddings and position information. The framework experiments on the SemEval 2014 dataset. The results of the experiments illustrate that injecting external knowledge into the Bidirectional Long Short-Term Memory network can improve classification accuracy significantly. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Aspect-based Opinion Mining is a fine-grained Sentiment Analysis method that models the relationship between aspect terms and context words using an Attention-based Bidirectional Long Short-Term Memory network. By incorporating a Sentiment Intensity Lexicon, the proposed framework improves classification accuracy by considering the interaction between aspects and context words.