Sentiment analysis of comments on youtube on react native vs flutter using the support vector machine and naïve bayes classifier algorithm
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Abstract
The study analyzed the sentiment of YouTube comments about React Native and Flutter, two popular platforms in cross-platform mobile app development, using the algorithms of Support Vector Machine and Naïve Bayes Classifier. The data was collected through YouTube comment scraping and analyzed using Google Colab with preprocessing techniques such as case folding, text cleaning, stopword removal, and stemming. Frequency-Inverse Term Document Frequency (TF-IDF) is used to extract features. The results showed that SVM was more effective in classifying positive and neutral sentiments, while NBC was superior in positive sentiment. Both algorithms have difficulty identifying negative sentiment, especially with the dominance of positive sentiment data. The study suggests the use of advanced strategies such as better feature selection or ensemble learning to improve the accuracy of sentiment classification on social media.
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