Dual Language Sentiment Analysis Model for YouTube Videos Ranking Based on Machine Learning Techniques
Abstract
YouTube is the biggest platform for social media video viewing, sharing web site about 500mints video uploaded on the YouTube in one second. In this huge data when the user search on the YouTube he can’t find desired results and if he found based on the keyword searched still, he can’t find the qualitative desired video content. Its takes too much time to get the desired video content. In Asia the two languages are mostly people use for communication 1. English, 2. Roman Urdu Our proposed works is on the dual language sentiment analysis of the of the YouTube video. Our proposed model helps the user to rank the video based on the English as well as Roman Urdu. We build a model this will help us to get the review in English and Roman Urdu this will combine them the score and rank the video based on English and Roman Urdu sentiment analysis. We deploy the different machine learning algorithms we find the best classifier for dual language sentiment analysis is logistic regression with count vectorization feature extraction with 87% model accuracy.
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