• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

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

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


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


  1. Khan A, Gul MA, Zareei M, Biswal RR, Zeb A, Naeem M, et al. Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm. Computational Intelligence and Neuroscience. 2020;p. 1–14. Available from: https://doi.org/10.1155/2020/7526580
  2. Obiedat R, Qaddoura R, Al-Zoubi AM, Al-Qaisi L, Harfoushi O, Alrefai M, et al. Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution. IEEE Access. 2022;10:22260–22273. Available from: https://doi.org/10.1109/access.2022.3149482
  3. Cui J, Wang Z, Ho SBB, Cambria E. Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review. 2023;56(8):8469–8510. Available from: https://doi.org/10.1007/s10462-022-10386-z
  4. Bordoloi M, Biswas SK. Sentiment analysis: A survey on design framework, applications and future scopes. Artificial Intelligence Review. 2023;56(11):12505–12560. Available from: https://doi.org/10. 1007/s10462-023-10442-2
  5. Bhargavi V, Khanam MH. A Comparative Study on Various Methodologies Used in Sentiment Analysis for Indian Languages. Artificial Intelligence and Sustainable Computing. 2023;p. 419–429. Available from: https://doi.org/10.1007/978-981-99-1431-9_33
  6. Pais S, Cordeiro J, Jamil ML. NLP-based platform as a service: a brief review. Journal of Big Data. 2022;9(1). Available from: https://doi.org/10.1186/s40537-022-00603-5
  7. Madani Y, Erritali M, Bouikhalene B. A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach. Multimedia Tools and Applications. 2023;82(18):27819–27838. Available from: https://doi.org/10.1007/s11042-023-14514-x
  8. Mostafa AM. Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset. Intelligent Automation & Soft Computing. 2023;35(1):1015–1034. Available from: https://doi.org/10.32604/iasc.2023.028041
  9. Aminimotlagh M, Shahhoseini H, Fatehi N. A reliable sentiment analysis for classification of tweets in social networks. Social Network Analysis and Mining. 2022;13(1). Available from: https://doi.org/10.1007/s13278-022-00998-2
  10. Cach ND, María NM, Fernando DP. Hybrid Deep Learning Models for Sentiment Analysis. Complexity. 2021;p. 1–16. Available from: https://doi.org/10.1155/2021/9986920
  11. Kaur G, Sharma A. HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization. Journal of Ambient Intelligence and Humanized Computing. 2023;14(9):11971–11984. Available from: https://doi.org/10.1007/s12652-022-03748-6
  12. Wankhade M, Rao ACS, Kulkarni C. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review. 2022;55(7):5731–5780. Available from: https://doi.org/10.1007/s10462-022-10144-1
  13. Mustafa AJI, Najadat AM, H. Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews. ACM Transactions on Asian and Low-Resource Language Information Processing. 2023;22(7):1–18. Available from: https://doi.org/10.1145/3605146
  14. Sumathi M, Parvin SA. Nuances of Data Pre-Processing and its Impact on Business. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). 2021. Available from: https://doi.org/10.1109/iciccs51141.2021.9432376
  15. Rehman AU, Malik AK, Raza B, Ali W. A Hybrid CNN-LSTM Model for Improving Accuracy of Movie Reviews Sentiment Analysis. Multimedia Tools and Applications. 2019;78(18):26597–26613. Available from: https://doi.org/10.1007/s11042-019-07788-7
  16. Gaye B, Zhang D, Wulamu A. A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique. Information. 2021;12(9):374. Available from: https://doi.org/10.3390/info12090374


© 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)


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