A sentimental analysis for youtube data using supervised learning approach

Abstract

As there are lots of social media applications which are getting closer to the users in very less time. So as the users are so much excited to interact with this application. There are lots of social media application which is booming are Facebook, Twitter, YouTube etc. So in this application users can not only see the content posted even they can also post their feelings what they are feeling after seeing the content. In YouTube there are lots of channels which are increasing day by day and the channel manager post the content according to their channel, so they need to analyze the customer's feedback or reviews which is posted on the contents. If these comments and feedback get analyzed the channel manager will get some decisions according to customers whether the customers are liking the content or not. If there is any requirement of changes in the content by looking at the reviews they can easily change. So for doing the sentiment analysis of customer reviews, different classification algorithm has been taken such as Decision Tree, K Nearest Neighbors and Support vector machine. Then the algorithm which is giving the highest accuracy is taken for building the model which will work as sentiment analysis model for other channel managers.