Why Sentiment Analysis-Based Metrics are Essential for Measuring Channel Performance on YouTube: An Experimental Study
Abstract
YouTube is a universal social medium that lets its users share videos and write comments on shared videos. Comments of the users on Youtube videos can be useful for YouTube channel owners, and it is worth analyzing them in terms of sentiment. The rate of like/dislike on a YouTube video is not sufficient for estimating the attitude of users towards it. This study proposes a three-step method for this attitude: In the first step, a sentiment analysis task is applied to the user comments on the videos of the YouTube channel "iJustin". In the second step, a new metric named Sentiment Index (SI) has been proposed and the Sentiment Indexes of the videos have been calculated. In the third step, an analysis is performed to show if the SI metric is time-independent. As a result, it was seen that most of the comments left on the videos (89%) have been written within the first 30 days after the video was published. Our experiments revealed that comments left more than 30 days after publishing the videos change the average SI values of the videos by only 0.4%, and in this respect, the SI metric is negligibly affected by the time parameter.
Keywords
Natural Language Processing, Sentiment Analysis, Data Science, Youtube, Opinion Mining, Social Media
Thanks
We want to thank Prof. Rahim Dehkharghani for his support.
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