Evaluation of Machine Learning Classifiers for Sentiment Analysis

Year : 2024 | Volume :11 | Issue : 02 | Page : –
By

Navdeep Bohra,

Ashish Kumari,

  1. Assistant Professor Department of computer science and engineering, Maharaja Surajmal Institute of Technology, New Delhi Delhi India
  2. Assistant Professor Department of computer science and engineering, Maharaja Surajmal Institute of Technology, New Delhi Delhi India

Abstract

Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this paper presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. Due to the inclusion of important data and non-useful characters (often called noise), applying models to them gets tricky. This article uses machine literacy to analyze Twitter sentiment analysis, the sentiment of tweets delivered from the Kaggle Twitter Sentiment Analysis dataset by creating a machine learning channel that employs three different models or categories, i.e., Logistic Regression (LR), Bernoulli Naive Bayes (Bernoulli NB), and Support Vector Machine (SVM) on Python. The accuracy and F1 Scores define to measure how well these classifiers function. In the comparison of the models through the accuracy outcome, Logistic Regression gives the most efficient results with an accuracy of 95.83% followed by SVM at 95.77%, it, in turn, performs better than Bernoulli Naive Bayes at 94.90% accuracy.

Keywords: Sentiment Analysis, Tweets, Bernoulli Naïve Bayes, Logistic Regression, Support Vector Machine.

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Navdeep Bohra, Ashish Kumari. Evaluation of Machine Learning Classifiers for Sentiment Analysis. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):-.
How to cite this URL: Navdeep Bohra, Ashish Kumari. Evaluation of Machine Learning Classifiers for Sentiment Analysis. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155853

References

  1. Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971.
  2. Mehta, P., & Pandya, S. (2020). A review on sentiment analysis methodologies, practices and applications. International Journal of Scientific and Technology Research, 9(2), 601-609.
  3. Brauwers, G., & Frasincar, F. (2022). A survey on aspect-based sentiment classification. ACM Computing Surveys, 55(4), 1-37.
  4. Bohra, N., & Bhatnagar, V. (2021). Group level social media popularity prediction by MRGB and Adam optimization. Journal of Combinatorial Optimization, 41, 328-347.
  5. Saberi, B., & Saad, S. (2017). Sentiment analysis or opinion mining: A review. Int. J. Adv. Sci. Eng. Inf. Technol, 7(5), 1660-1666.
  6. Bohra, N., Bhatnagar, V., Choudhary, A., Ahlawat, S., Sheoran, D., & Kumari, A. (2023). Popularity Prediction of Social Media Post Using Tensor Factorization. Intelligent Automation & Soft Computing, 36(1).
  7. Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12), 2009.
  8. Barbosa, L., & Feng, J. (2010, August). Robust sentiment detection on twitter from biased and noisy data. In Coling 2010: Posters(pp. 36-44).
  9. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011, June). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011)(pp. 30-38).
  10. Liang, P. W., & Dai, B. R. (2013, June). Opinion mining on social media data. In 2013 IEEE 14th international conference on mobile data management(Vol. 2, pp. 91-96). IEEE.
  11. Ren, B., & Cheng, L. (2009, May). Research of classification system based on naive bayes and metaclass. In 2009 Second International Conference on Information and Computing Science(Vol. 3, pp. 154-156). IEEE.
  12. Alsaeedi, A., & Khan, M. Z. (2019). A study on sentiment analysis techniques of Twitter data. International Journal of Advanced Computer Science and Applications, 10(2), 361-374.
  13. Kewsuwun, N., & Kajornkasirat, S. (2022). A sentiment analysis model of agritech startup on Facebook comments using naive Bayes classifier. International Journal of Electrical & Computer Engineering (2088-8708), 12(3).
  14. Patil, R. S., & Kolhe, S. R. (2022). Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets. Social Network Analysis and Mining, 12(1), 51.
  15. Bagheri, H., Torkamani, M. A., & Ghaffari, Z. (2014). Multi-agent approach for facing challenges in ultra-large scale systems. International Journal of Electrical and Computer Engineering, 4(2), 151.
  16. Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior, 31, 527-541.
  17. Burigo, M., & Schultheis, H. (2018). The effects of direction and orientation of located objects on spatial language comprehension. Language and Cognition, 10(2), 298-328.
  18. Kersbergen, B., & Schelter, S. (2021, April). Learnings from a Retail Recommendation System on Billions of Interactions at bol. com. In 2021 IEEE 37th International Conference on Data Engineering (ICDE)(pp. 2447-2452). IEEE.
  19. Azzouza, N., Akli-Astouati, K., Oussalah, A., & Bachir, S. A. (2017, June). A real-time Twitter sentiment analysis using an unsupervised method. In proceedings of the 7th international conference on web intelligence, mining and semantics(pp. 1-10).
  20. Aliman, G., Nivera, T. F. S., Olazo, J. C. A., Ramos, D. J. P., Sanchez, C. D. B., Amado, T. M., … & Valenzuela, I. C. (2022). Sentiment analysis using logistic regression. Journal of Computational Innovations and Engineering Applications, 7(1), 35-40.
  21. Fernández-Gavilanes, M., Álvarez-López, T., Juncal-Martínez, J., Costa-Montenegro, E., & González-Castaño, F. J. (2016). Unsupervised method for sentiment analysis in online texts. Expert Systems with Applications, 58, 57-75.
  22. Kaur, V. D. (2018, August). Sentimental analysis of book reviews using unsupervised semantic orientation and supervised machine learning approaches. In 2018 Second International conference on green computing and internet of things (ICGCIoT)(pp. 519-524). IEEE.
  23. Santos, M. L. B. D. (2022). The “so-called” UGC: an updated definition of user-generated content in the age of social media. Online Information Review, 46(1), 95-113.
  24. Bohra, n., & Bhatnagar, v. (2022). Social media popularity prediction by ttm clustered bayesian factorization. 21, no. 6, pp. 3293-3302, 2022.
  25. Ramadhan, W. P., Novianty, S. A., & Setianingsih, S. C. (2017, September). Sentiment analysis using multinomial logistic regression. In: International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pp. 46-49, IEEE(2017).
  26. Luo, Z., Osborne, M., & Wang, T. (2015). An effective approach to tweets opinion retrieval. World Wide Web, 18, 545-566.
Regular Issue Subscription Original Research
Volume 11
Issue 02
Received May 24, 2024
Accepted July 6, 2024
Published July 10, 2024
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