Supervisor
Dr Muhammad Iqbal
Programme
MSc in Data Analytics
Abstract
This study examines the role of sentiment analysis in customer queries, emphasising its impact on brand perception and the risks of poor query management. It compares the performance of NLP models—NLTK, spaCy, BERT, and DistilBERT—on customer query and feedback data. The findings show that BERT and DistilBERT produce similar results, often categorising queries as neutral, indicating their strength in handling diverse sentiments. NLTK and spaCy also share performance patterns. The research offers insights into the capabilities and limitations of these models in sentiment analysis.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
De Camillis, Patrizia, "Analysing Natural Language Processing Techniques: A Comparative Study of NLTK, spaCy, BERT, and DistilBERT on Customer Query Datasets." (2024). ICT. 52.
https://arc.cct.ie/ict/52