Sentiment Analysis Over Yelp Dataset

Sisal, Akhil Bharat (2023) Sentiment Analysis Over Yelp Dataset. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study investigate the Sentiment Analysis for a Business Recommendation System for businesses that use Sentiment Analysis to make the buying experience better for customers. The system uses a set of around 100,000 Yelp reviews to look at customer ratings, comments, and reviews in order to make personalised suggestions. By using information from customer data, this method goes beyond traditional recommendation algorithms. The study uses advanced feature extraction methods like TF-IDF vectorization and BERT tokenization, along with machine learning techniques like DBScan, K-means, and K-Nearest Neighbor (KNN), to rate these models on their accuracy, precision, recall, and F1-score. The results show that the KNN model works best, especially when BERT tokenization is used. The model developed shows great potential. The project’s successfully opens the door for more research into personalised retail solutions that meet customers’ changing needs.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour H Social Sciences > HF Commerce > Customer Service Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	23 May 2025 10:27
Last Modified:	23 May 2025 10:27
URI:	https://norma.ncirl.ie/id/eprint/7617

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