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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Analyzing Social Media Data using Sentiment Mining and Bi-gram Analysis for the Recommendation of YouTube Videos.

McGarry, Kenneth (2023) Analyzing Social Media Data using Sentiment Mining and Bi-gram Analysis for the Recommendation of YouTube Videos. Information, 14 (7). ISSN E2078-2489

Item Type: Article

Abstract

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts and sentiment.

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More Information

Uncontrolled Keywords: recommender systems; graph theory; sentiment analysis; Twitter; Reddit, YouTube
Related URLs:
Depositing User: Kenneth McGarry

Identifiers

Item ID: 16434
Identification Number: https://doi.org/10.3390/info14070408
ISSN: E2078-2489
URI: http://sure.sunderland.ac.uk/id/eprint/16434
Official URL: https://www.mdpi.com/2078-2489/14/7/408

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

Catalogue record

Date Deposited: 31 Jul 2023 10:35
Last Modified: 09 Aug 2023 08:05

Contributors

Author: Kenneth McGarry ORCID iD
Author: Kenneth McGarry

University Divisions

Faculty of Technology > School of Computer Science

Subjects

Computing > Data Science
Computing > Artificial Intelligence
Computing

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