Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer

Title

Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer

Creator

Abhijith S.; Pani A.K.

Description

After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-1112 LNNS, pp. 115-126.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Dataframes; Google API V3; Lemmatization; Live web scraping; NLP; Pandas; Sentiment analysis; Stemming; Tokenization; VADER

Coverage

Abhijith S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Pani A.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981976683-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-6684-0_11

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215665758&doi=10.1007%2f978-981-97-6684-0_11&partnerID=40&md5=bc94ae2cd74433d04dcc0e20a4a21ce0