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 |
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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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Uncontrolled Keywords: recommender systems; graph theory; sentiment analysis; Twitter; Reddit, YouTube |
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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 |
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Catalogue record
Date Deposited: 31 Jul 2023 10:35 |
Last Modified: 09 Aug 2023 08:05 |
Author: | Kenneth McGarry |
Author: | Kenneth McGarry |
University Divisions
Faculty of Technology > School of Computer ScienceSubjects
Computing > Data ScienceComputing > Artificial Intelligence
Computing
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