Date of Award
Master of Science in Computer Science
School of Computer Science and Engineering
First Reader/Committee Chair
Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural network model that learns from the review stars and then compare the neural network’s performance against both the Opinion Lexicon and TextBlob’s classification methods. We see that the accuracy of the Opinion Lexicon classification method is 64.38% while the accuracy with TextBlob’s classification method is 65.71% and the neural network model achieves an accuracy of 96.46%. The model would help brands for future reviews left by customers by classifying them as positive, negative, or neutral.
Nazareth, Brian, "REVIEW CLASSIFICATION USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING" (2023). Electronic Theses, Projects, and Dissertations. 1821.