Abstract
Social media produces enormous amounts of unutilized data. Through machine learning and text analytics techniques, organizations and individuals alike can use such data to extract a great deal of information. The type of information can be used in a plethora of ways; with techniques such as sentiment analysis allowing organizations to determine public reception to their products or finding trendy topics through the application of topic extraction. This paper outlines the process taken to develop a machine learning model that analyzes social media posts and extracts the post’s sentiment and topic. A multinomial Naïve Bayes model and a random forest model were trained for each objective to gauge which learning algorithm performs better with accuracy and F-Score being performance metrics. The results showcased that even with relatively simple implementations, and the analysis results on unseen social media posts produced promising accuracy results with short training time, with the multinomial Naïve Bayes model outperforming the random forest model.
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Elyassami, S., Albloushi, S., Alnuaimi, M.A., Alhosani, O., Al Ali, H., Almarashda, K. (2022). Intelligent Models for Mining Social Media Data. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_17
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