TED Talks Comments Sentiment Classification Using Machine Learning Algorithms

Abstract

This research presents a comparative analysis of sentiment analysis techniques applied to user comments on YouTube, with a specific focus on TED talks. The proliferation of social media platforms has provided individuals with unprecedented opportunities to express their opinions and emotions. YouTube, as a leading video-sharing platform, has become a significant hub for user-generated content and discussions on a wide range of topics. In light of the exponential growth of unstructured and semi-structured data, sentiment analysis plays a critical role in extracting valuable emotional insights from online interactions. To evaluate sentiments expressed in YouTube comments, a self-created and meticulously labeled dataset comprising user comments was employed. The study compared the performance of five ML techniques: NB, SVM, RF, KNN, and DT. The performance of the classifiers was evaluated using key evaluation metrics such as Precision, Recall, and F1-score. The findings of this research offer valuable insights into the efficacy of various machine learning techniques for sentiment analysis in the context of YouTube comments on TED talks. Among the classifiers, SVM demonstrated the highest Precision, Recall, and F1-score, indicating its effectiveness in accurately identifying sentiment in YouTube comments. Random Forest and Decision tree also displayed competitive performance, while KNN and Naïve Bayes exhibited slightly lower accuracy. These results provide researchers and practitioners with valuable information to make informed decisions regarding the selection of appropriate ML techniques for SA tasks on social media platforms.