AI-Enhanced Product Recommendation System using YouTube Comments Analysis

George, Sharon (2023) AI-Enhanced Product Recommendation System using YouTube Comments Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

This project’s aims to improve any AI driven product recommendations by analysing the YouTube comments and creating a user friendly Recommendation System. Traditional recommendation systems often struggle to adapt to and understand user sentiments quickly. To overcome this, we use YouTube comments as a primary data source and employ the Convolutional Long Short-Term Memory (CLSTM) algorithm for sentiment analysis. The project is unique because it uses real time YouTube comments to create a dynamic dataset for the recommendation system. The CLSTM algorithm is really good at figuring out feelings in text. It gives a lot of details about what users like and tries to make the product suggestions even better by getting more accurate and personalised. In conclusion this project uses sentiment analysis, artificial intelligence and recommendation systems to make recommendations. The CLSTM algorithm in the project performs well with an accuracy of 89%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jain, Mayank UNSPECIFIED
Uncontrolled Keywords:	AI; recommendation systems; YouTube; CLSTM; Comments
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 May 2025 11:27
Last Modified:	08 May 2025 11:27
URI:	https://norma.ncirl.ie/id/eprint/7514

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