Multilingual Sentiment Analysis for Mobile Gaming: A Comparative Study of Machine Learning and Hybrid Deep Learning Approaches

References

1. S. Ngah, S. Rahi, F. Long, C. Gabarre, A. Rashid, and A. H. Ngah, “Future behavioural of console gamers and mobile gamers: are they differ?,” Qual. Quant., pp. 1–27, 2024.
2. Newzoo, “Global esports market report.” [Online]. Available: https://newzoo.com/products/reports/global-esports-market-report
3. J. Anvar Shathik and K. Krishna Prasad, “A literature review on application of sentiment analysis using machine learning techniques,” Int J Appl Eng Manag Lett, vol. 4, no. 2, pp. 41–67, 2020.
4. K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput., vol. 97, p. 106754, 2020.
5. A. Modi, K. Shah, S. Shah, S. Patel, and M. Shah, “Sentiment analysis of Twitter feeds using flask environment: A superior application of data analysis,” Ann. Data Sci., vol. 11, no. 1, pp. 159–180, 2024.
6. A. Alslaity and R. Orji, “Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions,” Behav. Inf. Technol., vol. 43, no. 1, pp. 139–164, 2024.
7. A. M. Mantika, A. Triayudi, and R. T. Aldisa, “Sentiment Analysis on Twitter Using Naïve Bayes and Logistic Regression for the 2024 Presidential Election,” SaNa J. Blockchain, NFTs Metaverse Technol., vol. 2, no. 1, pp. 44–55, 2024.
8. M. Tabany and M. Gueffal, “Sentiment analysis and fake amazon reviews classification using SVM supervised machine learning model,” J. Adv. Inf. Technol., vol. 15, no. 1, pp. 49–58, 2024.
9. F. Y. Dharta, A. J. Mahardhani, S. R. Yahya, A. Dirsa, and E. M. Usulu, “Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment Towards the Presidential Election Phase,” J. Inf. dan Teknol., pp. 176–181, 2024.
10. F. Alhaek, W. Liang, T. M. Rajeh, M. H. Javed, and T. Li, “Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach,” Knowledge-Based Syst., vol. 286, p. 111406, 2024.
11. I. K. S. Al-Tameemi, M.-R. Feizi-Derakhshi, S. Pashazadeh, and M. Asadpour, “A comprehensive review of visual–textual sentiment analysis from social media networks,” J. Comput. Soc. Sci., pp. 1–72, 2024.
12. P. Chauhan, N. Sharma, and G. Sikka, “On the importance of pre-processing in small-scale analyses of twitter: a case study of the 2019 Indian general election,” Multimed. Tools Appl., vol. 83, no. 7, pp. 19219–19258, 2024.
13. S. Jain and P. K. Roy, “E-commerce review sentiment score prediction considering misspelled words: A deep learning approach,” Electron. Commer. Res., vol. 24, no. 3, pp. 1737–1761, 2024.
14. Y. S. Soekamto, I. Maryati, C. Christian, and E. Kurniawan, “Improving Publishing: Extracting Keywords and Clustering Topics,” J. Appl. Data Sci., vol. 5, no. 2, pp. 412–423, 2024.
15. S. Wu and Y. Gao, “Machine learning approach to analyze the sentiment of airline passengers’ tweets,” Transp. Res. Rec., vol. 2678, no. 2, pp. 48–56, 2024.
16. A. Zadgaonkar and A. J. Agrawal, “An Approach for analyzing unstructured text data using topic modeling techniques for efficient information extraction,” New Gener. Comput., vol. 42, no. 1, pp. 109–134, 2024.
17. N. A. Sharma, A. B. M. S. Ali, and M. A. Kabir, “A review of sentiment analysis: tasks, applications, and deep learning techniques,” Int. J. Data Sci. Anal., pp. 1–38, 2024.
18. M. Mujahid et al., “Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering,” J. Big Data, vol. 11, no. 1, p. 87, 2024.
19. A. U. Rehman, A. K. Malik, B. Raza, and W. Ali, “A hybrid CNN-LSTM model for improving accuracy of movie reviews sentiment analysis,” Multimed. Tools Appl., vol. 78, pp. 26597–26613, 2019.
20. A. H. Ombabi, W. Ouarda, and A. M. Alimi, “Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks,” Soc. Netw. Anal. Min., vol. 10, pp. 1–13, 2020.

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