Scopus Indexed Publications

Paper Details


Title
Performance Comparison of Multiple Supervised Learning Algorithms for YouTube Exaggerated Bangla Titles Classification
Author
Mirajul Islam, Jannatul Ferdous Ani, Abu Kaisar Mohammad Masum, Nushrat Jahan Ria,
Email
Abstract
Exaggerated titles of YouTube videos are annoying. YouTube has been the most popular online video service for several years, with billions of subscribers and viewers. These videos have titles. To attract audiences, the YouTuber uses the most interesting words in the video's title. On YouTube, there are two types of titles: consistent and exaggerated. Usually, these exaggerated titles encourage people to watch videos but in reality, people don't get much entertainment by watching these exaggerated titles videos. To overcome this problem, we present a Bangla text classification analysis approach based on Natural Language Processing (NLP) on YouTube titles. We collect two different phases of video data on YouTube after watching so many videos. This classification assists in the discovery of YouTube videos with exaggerated titles. We used a total of six models in this research. Based on the results of research conducted, Convolutional Neural Network (CNN) has successfully classified the exaggerated Bangla title videos because it achieves results of 80%, 81.25%, and 76.47% for accuracy, precision, and F1 Score respectively.

Keywords
YouTube , Convolutional Neural Network (CNN) , Natural Language Processing (NLP) , Machine Learning , Bangla Text Classification
Journal or Conference Name
2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
Publication Year
2021
Indexing
scopus