Sentiment Analysis from Twitter Dataset Using an Integrated Deep Learning Algorithms

Authors

  • Arjun Kuruva Research Scholar, Department Of Computer Science & Engineering YSR Engineering College of Yogi Vemana University, Proddatur, YSR (Dist.), A.P.
  • C. Nagaraju Professor & Principal Department Of Computer Science & Engineering YSR Engineering College of Yogi Vemana University, Proddatur, YSR (Dist.), A.P.

Keywords:

GRU, RoBERTa, Transformer, deep learning, sentiment analysis, NLP

Abstract

Sentiment analysis of customers or clients is critical for different government and private organizations to strengthen their relationships with customers or clients. The transformer model is represented by the robustly optimised BERT pre-training strategy (Roberta), which combines the advantages of both the RNN and GRU. The GRU model solves the vanishing gradients problem and shows how the embedding changes over time. Furthermore, this paper suggests using word embedding for data augmentation, which involves oversampling minority classes, to address the problem of imbalanced datasets in sentiment analysis. The popular sentiment analysis dataset used in this project is the Twitter US Airline Sentiment dataset with the RoBERTa-GRU-CNN model.

Downloads

Download data is not yet available.

References

Ligthart, A.; Catal, C.; Tekinerdogan, B. Systematic reviews in sentiment analysis: A tertiary study. Artif. Intell. Rev. 2021, 54, 4997–5053

Birjali, M.; Kasri, M.; Beni-Hssane, A. A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowl.-Based Syst. 2021, 226, 107134.

Bahdanau, D.; Cho, K.; Bengio, Y. Neural machine translation by jointly learning to align and translate. arXiv 2014, arXiv:1409.0473.

Hemakala, T.; Santhoshkumar, S. Advanced classification method of twitter data using sentiment analysis for airline service. Int. J. Comput. Sci. Eng. 2018, 6, 331–335.

Makhmudah, U.; Bukhori, S.; Putra, J.A.; Yudha, B.A.B. Sentiment Analysis Of Indonesian Homosexual Tweets Using Support Vector Machine Method. In Proceedings of the 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Jember, Indonesia, 16–17 October 2019; pp. 183–186.

Tariyal, A.; Goyal, S.; Tantububay, N. Sentiment Analysis of Tweets Using Various Machine Learning Techniques. In Proceedings of the 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 28–29 December 2018; pp. 1–5.

Gupta, A.; Singh, A.; Pandita, I.; Parashar, H. Sentiment analysis of Twitter posts using machine learning algorithms. In Proceedings of the 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 13–15 March 2019; pp. 980–983.

Jemai, F.; Hayouni, M.; Baccar, S. Sentiment Analysis Using Machine Learning Algorithms. In Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin, China, 28 June–2 July 2020; pp. 775–779.

Ramadhani, A.M.; Goo, H.S. Twitter sentiment analysis using deep learning methods. In Proceedings of the 2017 7th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia, 1–2 August 2017; pp. 1–4.

Raza, G.M.; Butt, Z.S.; Latif, S.; Wahid, A. Sentiment Analysis on COVID Tweets: An Experimental Analysis on the Impact of Count Vectorizer and TF-IDF on Sentiment Predictions using Deep Learning Models. In Proceedings of the 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), Islamabad, Pakistan, 20–21 May 2021; pp. 1–6

Demirci, G.M.; Keskin, ¸S.R.; Do ˘gan, G. Sentiment analysis in Turkish with deep learning. In Proceedings of the 2019 IEEE International Conference on Big Data, Los Angeles, CA, USA, 9–12 December 2019; pp. 2215–2221..

Rhanoui, M.; Mikram, M.; Yousfi, S.; Barzali, S. A CNN-BiLSTM model for document-level sentiment analysis. Mach. Learn. Knowl. Extr. 2019, 1, 832–847.

Tyagi, V.; Kumar, A.; Das, S. Sentiment Analysis on Twitter Data Using Deep Learning approach. In Proceedings of the 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, 18–19 December 2020; pp. 187–190.

Jang, B.; Kim, M.; Harerimana, G.; Kang, S.u.; Kim, J.W. Bi-LSTM model to increase accuracy in text classification: Combining Word2vec CNN and attention mechanism. Appl. Sci. 2020, 10, 5841.

Yang, Y. Convolutional neural networks with recurrent neural filters. arXiv 2018, arXiv:1808.09315.

Harjule, P.; Gurjar, A.; Seth, H.; Thakur, P. Text classification on Twitter data. In Proceedings of the 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India, 7–8 February 2020; pp. 160–164.

Zhang, X.; Zhao, J.; LeCun, Y. Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst. 2015, 28, 649–657.

Kafle, K.; Yousefhussien, M.; Kanan, C. Data augmentation for visual question answering. In Proceedings of the 10th International Conference on Natural Language Generation, Santiago de Compostela, Spain, 4–7 September 2017; pp. 198–202.

Wang, W.Y.; Yang, D. That is so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 17–21 September 2015; pp. 2557–2563.

AlSalman, H. An improved approach for sentiment analysis of arabic tweets in twitter social media. In Proceedings of the 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 19–21 March 2020; pp. 1–4.

Downloads

Published

24.03.2024

How to Cite

Kuruva, A. ., & Nagaraju, C. . (2024). Sentiment Analysis from Twitter Dataset Using an Integrated Deep Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 159–165. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4961

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.