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A critical empirical evaluation of deep learning models for solving aspect based sentiment analysis

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Abstract

Aspect-based sentiment analysis (ABSA) has captured great attention from researchers and industrialists owing to their pulverized nature of sentiment analysis task and the goal to anticipate sentiment polarity of given aspect of provided text. Massive growth nudged the researchers to innovate methodologies and strategies for every distinct research analysis question which could muddle through the impending concerns and composite schema of ABSA. The exponential growth of deep learning has extensively labeled this task with several Deep Neural Network (DNN) models. This survey article furnishes a comparative review about the proposed cutting-edge deep learning methods to solve an ABSA problem infusing the common exemplar datasets, assessment metrics and available performance analysis of deep-learning methods. The critical analysis of the materialized current solutions has proposed future research pathways for researchers and hence is instrumental for tweaking sentiment classification at aspect-level.

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  1. School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, Chennai, India

    P. R. Joe Dhanith & K. S. Sakunthala Prabha

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  1. P. R. Joe Dhanith
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Dhanith, P.R.J., Prabha, K.S.S. A critical empirical evaluation of deep learning models for solving aspect based sentiment analysis. Artif Intell Rev 56, 13127–13186 (2023). https://doi.org/10.1007/s10462-023-10460-0

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