Qualitative Sentiment Analysis of YouTube Contents based on User Reviews

Abstract

Sentiment analysis (a branch of Natural Language Processing) is one of the most crucial components of assessing online contents in current digital age. One of the leading competitors in the digital content market is YouTube, where thousands of new videos are uploaded every day. To assess the opinions of the users and customize advertisements and future contents accordingly, an appropriate sentiment analysis model is very crucial. Therefore, this study proposes a novel combination of deep learning technique called Encoder-decoder based Attention model to conduct sentiment analysis on YouTube reviews. In the proposed approach, the squeeze-and-excitation attention layer is utilized. Nearly 7,00,000 user comments from 8000 YouTube channels are utilized to train and test the proposed model in this study. Three different sentiments-positive, negative, and neutral—are assigned to the comments in the dataset for training purposes. The results show that the maximum accuracy of the model is 92.8% whereas maximum F1-score is 91.9%, surpassing several ML based state-of-the-art approaches. The evaluated metrics verify the balanced performance of the proposed approach. Moreover, the impact of the attention mechanism on the proposed approach is also assessed. Model accuracy for sentiment analysis is significantly improved by combining attention mechanism with encoder-decoder. As a generalized framework, this research can serve as a valuable guideline for future sentiment analysis on different languages.