A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

S Ashika Parvin; M Sumathi; R Barani

doi:10.17485/IJST/v16i44.2498

Article

A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i44.2498

Year: 2023, Volume: 16, Issue: 44, Pages: 3962-3970

Original Article

A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

S Ashika Parvin^1*, M Sumathi¹, R Barani²

¹Sri Meenakshi Government Arts College for Women (A), Madurai, India
²V.V Vanniaperumal College for Women, Virudhunagar, India

*Corresponding Author
Email: [email protected]

Received Date:02 October 2023, Accepted Date:13 October 2023, Published Date:17 October 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The objective of this study is to introduce an innovative hybrid approach that incorporates CNN and Bi-LSTM models to provide a solution to the sentiment analysis problem. The HCNN-BiLSTM Model is the acronym that we present for this methodology. Methods: Pre-processing, feature extraction, and sentiment classification are the three steps in this procedure. In the pre-processing stage, unneeded data gathered from the source text reviews is filtered out utilizing NLP systems. The prior studies presented an integrated strategy referred to as RBDT, which generates particular feature sets depending on the examination, for effectively extracting features. Next, sentiments are predicted using the proposed cutting-edge HCNN-BiLSTM model and grouped various sentimental phrases into five main groups: interest, sadness, anger, happiness, and disinterest. Findings: The findings showed that in terms of F-measure, accuracy, word count, and computational time, this suggested the HCNN-BiLSTM Model operates better than conventional deep learning (CNN) and machine learning techniques (SVM). Novelty: This proposed approach uses advanced methods on five review datasets, which include the Amazon dataset, Spotify app reviews, FIFA World Cup reviews, COVID-19 Vaccination reviews, and ChatGPT reviews, to produce competitive outcomes.

Keywords: Sentiment Analysis, Long ShortTerm Memory, Convolutional Neural Network, Natural Language Processing, Machine Learning, Deep Learning

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Copyright

© 2023 Parvin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)