Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Ashwani Gupta; Utpal Sharma

doi:10.17485/IJST/v17i7.2766

Article

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

VIEWS 240
PDF 66

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i7.2766

Year: 2024, Volume: 17, Issue: 7, Pages: 625-634

Original Article

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Ashwani Gupta^1*, Utpal Sharma²

¹Research Scholar, Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, India
²Professor, Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, India

*Corresponding Author
Email: [email protected]

Received Date:01 November 2023, Accepted Date:09 January 2024, Published Date:14 February 2024

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

Abstract

Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field.

Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi

References

Orvieto A, Smith S, Gu A, Fernando A, Gülçehre Ç, Pascanu R, et al. Resurrecting Recurrent Neural Networks for Long Sequences. 2023. Available from: https://doi.org/10.48550/arXiv.2303.06349
Bidirectional LSTM in NLP. GeeksforGeeks. Available from: https://www.geeksforgeeks.org/bidirectional-lstm-in-nlp/
Kumar D, Gupta A, Gupta VK, Gupta A. Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches. International Journal on Recent and Innovation Trends in Computing and Communication. 2023;11(5s):118–138. Available from: https://doi.org/10.17762/ijritcc.v11i5s.6636
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
Shrivash BK, Verma D, Pandey P. An Effective Framework for Sentiment Analysis of Hindi Sentiments Using Deep Learning Technique. Wireless Personal Communications. 2023;132:2097–2110. Available from: https://doi.org/10.1007/s11277-023-10702-y
Pathak A, Kumar S, Roy PP, Kim BG. Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models. Electronics. 2021;10(21):1–15. Available from: https://doi.org/10.3390/electronics10212641
Sahoo C, Wankhade M, Singh BK. Sentiment analysis using deep learning techniques: a comprehensive review. International Journal of Multimedia Information Retrieval. 2023;12(2). Available from: https://doi.org/10.1007/s13735-023-00308-2
Dhasarathan C, Shrestha H. An NLP Based Sentimental Analysis and Prediction: A Dynamic Approach. In: International Conference on Communication, Networks and Computing CNC 2020, Communications in Computer and Information Science. Springer, Singapore. 1502:343–353.
Akhtar MS, Ekbal A, Bhattacharyya P. Aspect based sentiment analysis in Hindi: resource creation and evaluation. In: Proceedings of the tenth international conference on language resources and evaluation (LREC'16). (pp. 2703-2709) European Language Resources Association (ELRA). 2016.
Gandhi H, Attar V. Extracting Aspect Terms using CRF and Bi-LSTM Models. Procedia Computer Science. 2020;167:2486–2495. Available from: https://doi.org/10.1016/j.procs.2020.03.301
Bhattacharya A, Debnath A, Shrivastava M. Enhancing Aspect Extraction for Hindi. In: Proceedings of The 4th Workshop on e-Commerce and NLP. (pp. 140-149) Association for Computational Linguistics. 2021.
Shrivastava K, Kumar S. A Sentiment Analysis System for the Hindi Language by Integrating Gated Recurrent Unit with Genetic Algorithm. The International Arab Journal of Information Technology. 2020;17(6):954–964. Available from: https://iajit.org/portal/PDF/November%202020,%20No.%206/18975.pdf#:~:text=It%20has%20been%20observed%20that,based%20and%20machine%20learning%20approaches.
Yadav V, Verma P, Katiyar V. Long short term memory (LSTM) model for sentiment analysis in social data for e-commerce products reviews in Hindi languages. International Journal of Information Technology. 2023;15(2):759–772. Available from: https://doi.org/10.1007/s41870-022-01010-y
Rani S, Kumar P. Aspect-based Sentiment Analysis using Dependency Parsing. ACM Transactions on Asian and Low-Resource Language Information Processing. 2021;21(3):1–19. Available from: https://doi.org/10.1145/3485243
Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications. 2023;82(3):3713–3744. Available from: https://doi.org/10.1007/s11042-022-13428-4
Salmony MY, Faridi AR, Masood F. Leveraging attention layer in improving deep learning models performance for sentiment analysis. International Journal of Information Technology. 2023. Available from: https://doi.org/10.1007/s41870-023-01570-7
Jurafsky D, Martin JH. Sequence Labeling for Parts of Speech and Named Entities. In: Speech and Language Processing. (Vol. 71) 2020.
Deep Learning | Introduction to Long Short Term Memory. geeksforgeeks.org. Available from: https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory/
Chen PH, Yu H, Dhillon IS, Hsieh C. DRONE: Data-aware Low-rank Compression for Large NLP Models. In: 35th Conference on Neural Information Processing Systems (NeurIPS 2021). (pp. 1-14) 2021.
Garbin C, Zhu X, Marques O. Dropout vs. batch normalization: an empirical study of their impact to deep learning. Multimedia Tools and Applications. 2020;79:12777–12815. Available from: https://doi.org/10.1007/s11042-019-08453-9
Hodson TO. Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development . 2022;15(14):5481–5487. Available from: https://doi.org/10.5194/gmd-15-5481-2022
Plevris V, Solorzano G, Bakas NP, Seghier MB. Investigation of performance metrics in regression analysis and machine learning-based prediction models. In: ECCOMAS Congress 2022 - 8th European Congress on Computational Methods in Applied Sciences and Engineering. CIMNE. 2022.

Copyright

© 2024 Gupta & Sharma. 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)