Advancing Sentiment Analysis during the Era of Data-Driven Exploration via the Implementation of Machine Learning Principles

Abstract

Information technology has seamlessly woven into the fabric of our daily existence, making it nearly inconceivable to envision life without the influence of social media platforms. Communication networks, encompassing mediums like television and radio broadcasts, have transcended their role as mere sources of entertainment, evolving into contemporary vehicles for disseminating significant information, viewpoints, and concepts among users. Certain subsets of this data hold pivotal importance, serving as valuable reservoirs for analysis and subsequent extraction of crucial insights, destined to inform future decision-making processes. Within the scope of this undertaking, we delve into the intricacies of sentiment analysis, leveraging the power of machine learning to prognosticate and dissect data derived from external origins. A prime focal point of this endeavor revolves around the implementation of the Naive Bayes technique, a supervised approach that imparts knowledge to the system, enabling it to forecast the emotional undercurrents of forthcoming input data. Empirical findings stemming from this venture substantiate the prowess of the Naive Bayes method, positioning it as a formidable and highly efficient tool in the arsenal of sentiment analysis methodologies. Its remarkable accuracy in discerning the positive and negative polarity of data reinforces its merit. Furthermore, this approach expedites the generation of high-caliber results within an abbreviated timeframe, setting it apart from alternative techniques and processes inherent in the realm of machine learning.

Keywords

• Sentiment Analysis
• Favorable Polarity
• Unfavorable Polarity
• Naive Bayes Technique
• Machine Learning
• Guided Training.

References

1. [1] R. Singh and R. Singh, “Applications of sentiment analysis and machine learning techniques in disease outbreak prediction–A review,” Mater. Today Proc., vol. 81, pp. 1006–1011, 2023.
2. [2] G. Gupta et al., “DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms,” Diagnostics, vol. 13, no. 6, p. 1093, 2023.
3. [3] E. M. Mercha and H. Benbrahim, “Machine learning and deep learning for sentiment analysis across languages: A survey,” Neurocomputing, vol. 531, pp. 195–216, 2023.
4. [4] H. Rahman, J. Tariq, M. A. Masood, A. F. Subahi, O. I. Khalaf, and Y. Alotaibi, “Multi-tier sentiment analysis of social media text using supervised machine learning,” Comput. Mater. Contin, vol. 74, pp. 5527–5543, 2023.
5. [5] M. Costola, O. Hinz, M. Nofer, and L. Pelizzon, “Machine learning sentiment analysis, COVID-19 news and stock market reactions,” Res. Int. Bus. Financ., vol. 64, p. 101881, 2023.
6. [6] Y. Wang et al., “Supervised Gradual Machine Learning for Aspect -Term Sentiment Analysis,” Trans. Assoc. Comput. Linguist., vol. 11, pp. 723– 739, 2023.
7. [7] M. A. Jassim, D. H. Abd, and M. N. Omri, “A survey of sentiment analysis from film critics based on machine learning, lexicon and hybridization,” Neural Comput. Appl., vol. 35, no. 13, pp. 9437–9461, 2023.
8. [8] S. Ghosh et al., “‘Do we like this, or do we like like this?’: Reflections on a Human-Centered Machine Learning Approach to Sentiment Analysis,” in International Conference on Human-Computer Interaction, Springer, 2023, pp. 63–82.

9. [9] Y. N. Prajapati, U. Sesadri, T. R. Mahesh, S. Shreyanth, A. Oberoi, and K. P. Jayant, “Machine Learning Algorithms in Big Data Analytics for Social Media Data Based Sentimental Analysis,” Int. J. Intell. Syst. Appl. Eng., vol. 10, no. 2s, pp. 264–267, 2022.
10. [10] A. Mitra and S. Mohanty, “Sentiment Analysis Using Machine Learning Approaches,” in Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 2, Springer, 2021, pp. 63–68.
11. [11] S. Qaiser, N. Yusoff, M. A. Remli, and H. K. Adli, “A comparison of machine learning techniques for sentiment analysis,” Turkish J. Comput. Math. Educ., 2021.
12. [12] J. Srivastava and N. Katiyar, “Machine Learning Technique for Target Based Sentiment Analysis,” in Micro-Electronics and Telecommunication Engineering, Springer, 2021, pp. 161–169.
13. [13] M. Hayouni and S. Baccar, “Sentiment analysis using machine learning algorithms,” 2021.
14. [14] A. A. Ansari, “Evolution of Sentiment Analysis: Methodologies and Paradigms,” Trends Data Sci. Appl. Theory Pract., pp. 147–174, 2021.
15. [15] M. H. I. Ahmad Hapez, N. L. Adam, and Z. Ibrahim, “Performance analysis of machine learning techniques for sentiment analysis,” in Advances in Visual Informatics: 7th International Visual Informatics Conference, IVIC 2021, Kajang, Malaysia, November 23–25, 2021, Proceedings 7, Springer, 2021, pp. 205–213.
16. [16] V. Umarani, A. Julian, and J. Deepa, “Sentiment analysis using various machine learning and deep learning Techniques,” J. Niger. Soc. Phys. Sci., pp. 385–394, 2021.
17. [17] A. Bhardwaj and P. Srivastava, “A machine learning approach to sentiment analysis on web based feedback,” in Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020, Springer, 2021, pp. 127–139.
18. [18] I. I. Mestric, A. Kok, G. Valiyev, M. Street, and P. Lenk, “Aspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports,” Inf. Secur., vol. 46, no. 3, pp. 227–238, 2020.
19. [19] A. Mehta, Y. Parekh, and S. Karamchandani, “Performance evaluation of machine learning and deep learning techniques for sentiment analysis,” in Information Systems Design and Intelligent Applications: Proceedings of Fourth International Conference INDIA 2017, Springer, 2018, pp. 463–471.
20. [20] O. Iparraguirre-Villanueva et al., “The public health contribution of sentiment analysis of Monkeypox tweets to detect polarities using the CNN-LSTM model,” Vaccines, vol. 11, no. 2, p. 312, 2023.
21. [21] B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and As. Perera, “Opinion mining and sentiment analysis on a twitter data stream,” in International conference on advances in ICT for emerging regions (ICTer2012), IEEE, 2012, pp. 182–188.
22. [22] F. Hallsmar and J. Palm, “Multi-class sentiment classification on twitter using an emoji training heuristic.” 2016.
23. [23] B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and As. Perera, “Opinion mining and sentiment analysis on a twitter data stream,” in International conference on advances in ICT for emerging regions (ICTer2012), IEEE, 2012, pp. 182–188.
24. [24] Z. Ke, H. Xu, and B. Liu, “Adapting BERT for continual learning of a sequence of aspect sentiment classification tasks,” arXiv Prepr. arXiv2112.03271, 2021.
25. [25] O. Iparraguirre-Villanueva et al., “The public health contribution of sentiment analysis of Monkeypox tweets to detect polarities using the CNN-LSTM
26. [26] F. Hallsmar and J. Palm, “Multi-class sentiment classification on twitter using an emoji training heuristic.” 2016.
27. [27] Sharma, D., & Sabharwal, M, “Sentiment analysis for social media using SVM classifier of machine learning. ” Int J Innov Technol Exploring Eng (IJITEE), 2019, 8(9), 39-47.