International Journal of Scientific & Engineering Research Volume 11, Issue 3, March-2020 492 ISSN 2229-5518 IJSER © 2020 http://www.ijser.org Analysis of YouTube of Videos: A Literature Survey Neha Reddy Piyush Gupta Prasham Mehta Puneet Gupta 4 th year student, Dept. of CSE 4 th year student, Dept. of CSE 4 th year student, Dept. of CSE 4 th year student, Dept. of CSE BMS College of Engineering BMS College of Engineering BMS College of Engineering BMS College of Engineering Bengaluru, Karnataka Bengaluru, Karnataka Bengaluru, Karnataka Bengaluru, Karnataka 1BM16CS057@bmsce.ac.in 1BM16CS066@bmsce.ac.in 1BM16CS070@bmsce.ac.in 1BM16CS072@bmsce.ac.in Vikranth BM Asst. Professor, Dept. of CSE BMS College of Engineering Bengaluru, Karnataka vikranthbm.cse@bmsce.ac.in Abstract— Consumption of content from YouTube (Lanyu Shang, 2019) and other OTT(over-the-top) platforms is constantly increasing. YouTube (Lanyu Shang, 2019) being a source of education, entertainment and promotion, is a very lucrative platform. YouTubers tend to unethically attract viewers into clicking their video by manipulating their title and/or thumbnail. In this paper we present a method to train a model to classify a video as Clickbait (Lanyu Shang, 2019) video or non-Clickbait (Lanyu Shang, 2019) video. Keywords—Clickbait, YouTube (Lanyu Shang, 2019) [1], Comments, Title, Thumbnail I. INTRODUCTION is increasingly becoming a major resource for sharing YouTube is becoming a major resource for sharing and consuming video content. It is gaining immense popularity and support from viewer community due to its comprehensive repository of videos. Also, it supports diversity by having different facets such as modals, languages, domains and cultures. For a YouTube (Lanyu Shang, 2019) content developer or a YouTuber with various notable channels, (Lanyu Shang, 2019) this is a profession with a lot of monetary potential. The younger generations are recently shifting to YouTube (Lanyu Shang, 2019) and other OTT platforms, away from the traditional television. A YouTube (Lanyu Shang, 2019) video often consists of a title, thumbnail, video content along with other non-video features. Despite it being unethical, content developers deliberately manipulate the heading and the thumbnail so as to attract more audience and baiting them into viewing their content. There are quite a few instances when the content of the video mismatches with the heading of the video or the thumbnail of the video. This is known as a Clickbait (Lanyu Shang, 2019)Video. Our aim is to classify a video as to whether it is a Clickbait (Lanyu Shang, 2019) or not. This is critically important as a majority of people spend their time on YouTube (Lanyu Shang, 2019) and not getting what they search for is a waste of their precious time. We use sentiment analysis on viewer comments to identify a video as click bait or not. II. DATASET We are only working with YouTube (Lanyu Shang, 2019) data that consists of viewer comments. The data is collected with the help of YouTube (Lanyu Shang, 2019) API v3. We created a Google Developer account and generated a key to extract all the details of a video in the form of a JSON file. This dataset contains all the details of the trending YouTube videos along with its likes, dislikes, comments, tags and views for each video for a particular year, which comprises a top-level comment and replies, if any exist, to that comment. IJSER