The State of the Art: Sentiment Analysis Using Different Tools and Techniques

Authors

  • L.Vetrivendan Department of Computer Science and Engineering, Alliance University, Bengaluru, Karnataka, India. Author
  • Arjun K P Department of Computer Science and Engineering, GITAM University, Bengaluru, Karnataka, India. Author

Keywords:

Machine learning, Sentiment Analysis, Algorithms, Tools and Techniques

Abstract

With technology and the Internet, people can share their opinions with everyone. People are now expressing their opinions and thoughts on something thanks to a new media. It's a big source of information where people may exchange and read others' perspectives, which can influence them. Companies and researchers use the Internet's opinion-rich data to understand public sentiment, which led to sentiment analysis. Thus, numerous academics have studied public emotions in this topic. This research helps the public by highlighting their sentiment and businesses by allowing them to analyze sentiment and develop strategy. Our purpose is to examine the academics' research to discover assumptions, methodological flaws, and future directions.

Downloads

Download data is not yet available.

References

Rodrigues, A. P., Fernandes, R., Shetty, A., Lakshmanna, K., & Shafi, R. M. (2022). Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. Computational Intelligence and Neuroscience, 2022.

Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022). A deep learning approach for sentiment analysis of COVID-19 reviews. Applied Sciences, 12(8), 3709.

Zahoor, K., Bawany, N. Z., & Hamid, S. (2020, November). Sentiment analysis and classification of restaurant reviews using machine learning. In 2020 21st International Arab Conference on Information Technology (ACIT) (pp. 1-6). IEEE.

Wassan, S., Chen, X., Shen, T., Waqar, M., & Jhanjhi, N. Z. (2021). Amazon product sentiment analysis using machine learning techniques. Revista Argentina de Clínica Psicológica, 30(1), 695.

Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. (2019). Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy, 21(6), 589.

Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754.

Qorib, M., Oladunni, T., Denis, M., Ososanya, E., & Cotae, P. (2023). COVID-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset. Expert Systems with Applications, 212, 118715.

Habimana, O., Li, Y., Li, R., Gu, X., & Yu, G. (2020). Sentiment analysis using deep learning approaches: an overview. Science China Information Sciences, 63, 1-36.

Yadav, A., & Vishwakarma, D. K. (2019). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6)

Neethu, M. S., & Rajasree, R. (2013, July). Sentiment analysis in twitter using machine learning techniques. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1-5). IEEE.

Singh, J., Singh, G., & Singh, R. (2017). Optimization of sentiment analysis using machine learning classifiers. Human-centric Computing and information Sciences, 7, 1-12.

Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., & Rehman, A. (2017). Sentiment analysis using deep learning techniques: a review. International Journal of Advanced Computer Science and Applications, 8(6).

Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017 (pp. 639-647). Springer Singapore.

Le, B., & Nguyen, H. (2015). Twitter sentiment analysis using machine learning techniques. In Advanced Computational Methods for Knowledge Engineering: Proceedings of 3rd International Conference on Computer Science, Applied Mathematics and Applications-ICCSAMA 2015 (pp. 279-289). Springer International Publishing.

Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), 45-49.

Published

2024-02-26

Issue

Section

Research Articles

How to Cite

L.Vetrivendan, and Arjun K P. 2024. “The State of the Art: Sentiment Analysis Using Different Tools and Techniques”. International Journal of Scientific and Research in Engineering(IJSRE) 1 (1): 32-39. http://ijsre.org/index.php/home/article/view/14.