SENTIMENT ANALYSIS ON YOUTUBE SMART PHONE UNBOXING VIDEO REVIEWS IN SRI LANKA
DOI:
https://doi.org/10.29121/granthaalayah.v10.i11.2022.4884Keywords:
Natural Language Processing, Text Analysis, Sentiment Analysis, Social Media Analysis, YouTubeAbstract [English]
Product-related reviews are based on users’ experiences that are mostly shared on videos in YouTube. It is the second most popular website globally in 2021. People prefer to watch videos on recently released products prior to purchasing, in order to gather overall feedback and make worthy decisions. These videos are created by vloggers who are enthusiastic about technical materials and feedback is usually placed by experienced users of the product or its brand. Analyzing the sentiment of the user reviews gives useful insights into the product in general. This study is focused on three smartphone reviews, namely, Apple iPhone 13, Google Pixel 6, and Samsung Galaxy S21 which were released in 2021. VADER, which is a lexicon and rule-based sentiment analysis tool was used to classify each comment to its appropriate positive or negative orientation. All three smartphones show a positive sentiment from the users’ perspective and iPhone 13 has the highest number of positive reviews. The resulting models have been tested using Naïve Bayes, Decision Tree, and Support Vector Machine. Among these three classifiers, Support Vector Machine shows higher accuracies and F1-scores.
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