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American Scientific Publishing Group

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Journal of Cognitive Human-Computer Interaction

ISSN
Online: 2771-1463 Print: 2771-1471
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Continuous publication

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Open access journal. All articles are freely available online with no APC.

Journal of Cognitive Human-Computer Interaction
Full Length Article

Volume 8Issue 1PP: 14-21 • 2024

A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars

Kumal Kumar 1* ,
Shivam Kumar 1
1Mizoram University, Aizawl-796004, India
* Corresponding Author.
Received: October 15, 2023 Revised: January 22, 2024 Accepted: April 17, 2024

Abstract

This paper explores sentiment analysis in Indian languages through a deep learning approach, combining machine learning techniques with natural language processing (NLP). Three neural network architectures—CNN, LSTM, and GRU—are employed to construct sentiment analysis models. Additionally, transfer learning is utilized via FastText, MURIL, and IndicBERT embeddings. The models are trained and evaluated on a translated dataset derived from the Sentiment140 dataset from Kaggle. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models. The study addresses the challenges of sentiment analysis in Indian languages by leveraging deep learning techniques and linguistic diversity, providing insights into sentiment analysis across diverse languages and cultures. Furthermore, this project extends its analysis to include work on Gujarati, Marathi, and Sindhi languages, contributing to the understanding of sentiment analysis in a broader spectrum of Indian languages

Keywords

Sentiment Analysis Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Gated Recurrent Units (GRUs) Deep Learning

References

[1] Rani, S., Kumar, P. A journey of Indian languages over sentiment analysis: a systematic review. Artif Intell Rev 52, 1415–1462 (2019).

[2] Wankhade, M., Rao, A.C.S. & Kulkarni, C. A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev 55, 5731–5780 (2022).

[3] Sajal Singhal, Gautam Pruthi, Ayush Kumar, Lakshay Kapoor, Vandana Bhatia. "Optimizing Election Result Prediction Through Fine-Tuned Transformer Models" , 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), 2023 

[4] Sujata Rani, Parteek Kumar. "A journey of Indian languages over sentiment analysis: a systematic review"  Artificial Intelligence Review, 2018

[5] Afifah Mohd Asri, Siti Rohaidah Ahmad, Nurhafizah Moziyana Mohd Yusop. "Feature Selection using Particle Swarm Optimization for Sentiment Analysis of Drug Reviews" , International Journal of Advanced Computer Science and Applications, 2023

[6]  Ajith Krishna R,Ankit Kumar,Vijay K. "An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT." Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 1, 2021 ,PP. 37-45.

[7] Rupali S. Patil, Satish R. Kolhe. "Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets" , Social Network Analysis and Mining, 2022

[8] Feriel Khennouche, Youssef Elmir, Yassine Himeur, Nabil Djebari, Abbes Amira. "Revolutionizing generative pre-traineds: Insights and challenges in deploying ChatGPT and generative chatbots for FAQs" , Expert Systems with Applications, 2024

[9] Anita Saroj, Akash Thakur, Sukomal Pal. "Sentiment analysis on Hindi tweets during COVID‐19 pandemic", Computational Intelligence, 2023

 

[10] Parvesh K,Tharun C,Prakash M. "Apparel Recommendation Engine Using Inverse Document Frequency and Weighted Average Word2vec." Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 2, 2021 ,PP. 46-56.

[11]  Debatosh Chakraborty, Dwijen Rudrapal, Baby Bhattacharya. "Chapter 17 A Study on the Research Progress of Multimodal Sentiment Analysis in Indian Languages" , Springer Science and Business Media LLC, 2023

[12] Rimah Amami, Rim Amami, Chiraz Trabelsi, Sherin Hassan Mabrouk, Hassan A. Khalil. "A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN  Architecture", MENDEL,  2023

Cite This Article

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Kumar, Kumal, Kumar, Shivam. "A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars." Journal of Cognitive Human-Computer Interaction, vol. Volume 8, no. Issue 1, 2024, pp. 14-21. DOI: https://doi.org/10.54216/JCHCI.080102
Kumar, K., Kumar, S. (2024). A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars. Journal of Cognitive Human-Computer Interaction, Volume 8(Issue 1), 14-21. DOI: https://doi.org/10.54216/JCHCI.080102
Kumar, Kumal, Kumar, Shivam. "A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars." Journal of Cognitive Human-Computer Interaction Volume 8, no. Issue 1 (2024): 14-21. DOI: https://doi.org/10.54216/JCHCI.080102
Kumar, K., Kumar, S. (2024) 'A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars', Journal of Cognitive Human-Computer Interaction, Volume 8(Issue 1), pp. 14-21. DOI: https://doi.org/10.54216/JCHCI.080102
Kumar K, Kumar S. A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars. Journal of Cognitive Human-Computer Interaction. 2024;Volume 8(Issue 1):14-21. DOI: https://doi.org/10.54216/JCHCI.080102
K. Kumar, S. Kumar, "A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars," Journal of Cognitive Human-Computer Interaction, vol. Volume 8, no. Issue 1, pp. 14-21, 2024. DOI: https://doi.org/10.54216/JCHCI.080102
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