Abstract

The usage of social media to interchange ideas and facts has increased exponentially due to technological advancements. Platforms for video sharing, like YouTube, have distinctive environments and architecture that people use for entertainment, education, and to keep themselves updated. YouTube is one of the most frequently used social media platforms, and users can connect to it by viewing, sharing opinions through comments, liking and disliking videos. A viewpoint or judgement formed about anything is referred to as an opinion. It can be collected and used to check knowledge, suggest the author with new video ideas, and analyze user behaviour. In this study, the data extracted from the free video-sharing platform YouTube concerning the ‘Air India Flight Urination Case’ was observed recently to recognize people’s opinions on national and international levels. Based on approximately 10,000 comments about the incident, models are applied to classify and investigate the sentiments. This investigation uses TF-IDF and Bag of Words (BoW) text modelling techniques and observed that BoW performs better than TF-IDF. Moreover, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machines, and some ensemble algorithms like Random Forests, Gradient Boost, and Voting Classifier combining (Support Vector Machine, Decision Tree, Logistic Regression and Random Forest) with soft and hard voting had been applied and found that Support Vector Machine has the highest classification accuracy of 84%.

Authors

Raj Kumar Singh¹, Ani Thomas²
Chhattisgarh Swami Vivekanand Technical University, India¹, Bhilai Institute of Technology, India²

Keywords

Natural Language Processing, Sentiment Analysis, Machine Learning, Ensemble, Flight, TF-IDF, Bag-of-Words