Opinion-Mining Technique on Generative Artificial Intelligence Topic Using Data Classification Algorithms
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The study employed an opinion-mining technique using data classification algorithms on the topic of Generative Artificial Intelligence (GenAI) to determine the sentiments of Twitter users. The researcher used a sentiment analysis framework to gather the datasets for dataset training and predict the results using Naïve Bayes, Random Forest, and SVM algorithms. The result shows that SVM and Random Forest algorithms had the same precision and recall of 1.000 indicating that the result has no false positive values. On the other hand, the Naïve Bayes algorithm garnered a .949 precision and .939 recall which means fewer false positive results on the trained models. The overall result shows that the trained datasets indicate a successful prediction with fewer false positive results. Moreover, the result of the sentiment analysis shows that more positive sentiments were drawn on the topic of generative artificial intelligence indicating the use and benefits of using AI. Furthermore, based on the result of the study, the research recommended the use of the sentiment analysis framework through an opinion-mining technique using data classification algorithms as it may help analyze different emotions of social media users.
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