Sentiment Evaluation of FlipKart Product Reviews using the Recurring Neural Network

Ghani, Usman (2024) Sentiment Evaluation of FlipKart Product Reviews using the Recurring Neural Network. Masters thesis, Dublin, National College of Ireland.

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Abstract

Sentiment analysis, a key aspect of natural language processing, is critical in deciphering user emotions from textual data. This paper presents a comprehensive exploration of sentiment analysis, focusing on the development and optimization of a deep learning model. Leveraging LSTM networks, our study involves training the model on diverse datasets, encompassing a range of sentiments. We delve into preprocessing techniques and feature engineering to enhance model robustness. Results showcase the model's effectiveness in classifying sentiments, with a particular emphasis on practical applications such as customer reviews and social media comments. The achieved accuracy, precision, and recall metrics demonstrate the model's potential for real-world implementations.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Del Rosal, Victor UNSPECIFIED
Uncontrolled Keywords:	Sentiment Analysis; Natural Language Processing; Deep Learning; LSTM; Customer Feedback; Text Classification
Subjects:	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 P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HF Commerce > Marketing
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Tamara Malone
Date Deposited:	07 Apr 2025 11:39
Last Modified:	07 Apr 2025 11:39
URI:	https://norma.ncirl.ie/id/eprint/7381

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