Abstract In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’ performance.
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Abirami, A. M., & Gayathri, V. (2017). A survey on sentiment analysis methods and approach. 2016 Eighth International Conference on Advanced Computing (ICoAC), 72–76. 10.1109/ICoAC.2017.7951748
Aydo, E., & Akcayol, M. A. (2016). Aydogan2016.Pdf.
Batanović, V., Nikolić, B., & Milosavljević, M. (2016). Reliable baselines for sentiment analysis in resource-limited languages: The serbian movie review dataset. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2688–2696.
Brar, G. S., & Sharma, A. (2018). Sentiment analysis of movie review using supervised machine learning techniques. International Journal of Applied Engineering Research, 13(16), 12788–12791.
Devika, M. D., & Sunitha Cͣand Ganesh, A. (2016). Sentiment analysis: a comparative study on different approaches. Procedia Computer Science, 87, 44–49. 10.1016/j.procs.2016.05.124
El Rahman, S. A., AlOtaibi, F. A., & AlShehri, W. A. (2019). Sentiment analysis of twitter data. 2019 International Conference on Computer and Information Sciences (ICCIS), 1–4. 10.1109/ICCISci.2019.8716464
Goularas, D., & Kamis, S. (2019). Evaluation of deep learning techniques in sentiment analysis from twitter data. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17. 10.1109/Deep-ML.2019.00011
Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. Advances in Intelligent Systems and Computing, 768, 639–647. 10.1007/978-981-13-0617-4_61 10.1007/978-981-13-0617-4_61
Mendi, A. F. (2022). A Sentiment Analysis Method Based on a Blockchain-Supported Long Short-Term Memory Deep Network. Sensors, 22(12), 4419. 10.3390/s22124419
Mishra, P., Rajnish, R., & Kumar, P. (2016). Sentiment analysis of Twitter data: Case study on digital India. 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect Your Worlds, 148–153. 10.1109/INCITE.2016.7857607
Mitra, A. (2020). Sentiment analysis using machine learning approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies (UCCT), 2(03), 145–152. 10.36548/jucct.2020.3.004
Moralwar, S. B., & Deshmukh, S. N. (2015). Different approaches of sentiment analysis. Int. J. Comput. Sci. Eng, 3(3), 160–165.
Pujari, C., Aiswarya, & Shetty, N. P. (2018). Comparison of classification techniques for feature oriented sentiment analysis of product review data. Data Engineering and Intelligent Computing: Proceedings of IC3T 2016, 149–158. 10.1007/978-981-10-3223-3_14
Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research. 10.1108/IntR-04-2016-0086
Raghuvanshi, N., & Patil, J. M. (2016). A brief review on sentiment analysis. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2827–2831. 10.1109/ICEEOT.2016.7755213
Samaras, L., García-Barriocanal, E., & Sicilia, M.-A. (2023). Sentiment analysis of COVID-19 cases in Greece using Twitter data. Expert Systems with Applications, 120577. 10.1016/j.eswa.2023.120577
Sentiment Analysis Challenges: Everything You Need to Know. (n.d.). Retrieved August 1, 2023, from https://www.repustate.com/blog/sentiment-analysis-challenges-with-solutions/
Sentiment Analysis Guide. (n.d.). Retrieved August 1, 2023, from https://monkeylearn.com/sentiment-analysis/
Shivaprasad, T. K., & Shetty, J. (2017). Sentiment analysis of product reviews: a review. 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 298–301. 10.1109/ICICCT.2017.7975207
Singh, J., Singh, G., & Singh, R. (2017). Optimization of sentiment analysis using machine learning classifiers. Human-Centric Computing and Information Sciences, 7, 1–12. 10.1186/s13673-017-0116-3
Singh, P. (2023). Systematic review of data-centric approaches in artificial intelligence and machine learning. Data Science and Management. 10.1016/j.dsm.2023.06.001
Smetanin, S., & Komarov, M. (2019). Sentiment analysis of product reviews in Russian using convolutional neural networks. 2019 IEEE 21st Conference on Business Informatics (CBI), 1, 482–486. 10.1109/CBI.2019.00062
Srivastava, S., Nagpal, A., & Bagwari, A. (2020). Various Approaches in Sentiment Analysis. 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), 92–96. 10.1109/CICN49253.2020.9242618
Tchalakova, M., Gerdemann, D., & Meurers, D. (2011). Automatic sentiment classification of product reviews using maximal phrases based analysis. Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011), 111–117.
Top 10 Machine Learning Algorithms With Python & R Code. (n.d.-a). Retrieved August 1, 2023, from https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
Vinodhini, G., & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282–292.
Y\ild\ir\im, E., Yazgan, H., Özbek, O., Günay, A. C., Kocaç\inar, B., \cSengel, Ö., & Akbulut, F. P. (2023). Sentiment Analysis of Tweets on Online Education during COVID-19. IFIP International Conference on Artificial Intelligence Applications and Innovations, 240–251. 10.1007/978-3-031-34111-3_21
Yang, P., & Chen, Y. (2017). A survey on sentiment analysis by using machine learning methods. 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 117–121. 10.1109/ITNEC.2017.8284920
Singh, N., & Jaiswal, U. C. (2023). Sentiment Analysis Using Machine Learning: A Comparative Study. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e26785. https://doi.org/10.14201/adcaij.26785
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