Determining Customer Sentiment in Fashion Product Reviews using Lexicon-based Approach and Machine Learning Techniques

Taiwo Olufemi Ayomide, William Alston

Abstract


Nowadays, E-commerce websites use online reviews to know what and how customers feel about their products. These reviews are essential to the growth and improvement of these companies. The fashion industry thrives on the feedback and perception of the user. Hence, the need to know the user's feelings and opinions after using their products. Sentiment analysis evaluates people's opinions from the reviews and helps the business make decisions based on its results. The analysis process involves natural language processing, text analysis, and opinion classification. The sentiment analysis in this project using a fashion review dataset was done using the lexicon- based approach and machine learning algorithms. The lexicon-based approach classified the sentiments as negative, neutral, and positive. The classification was done using the polarity score of reviews, and the NLP techniques used are TextBlob and VADER sentiments. The machine learning algorithms used are Logistic Regression, SVM, and AdaBoost. The logistic regression model and AdaBoost showed higher accuracy and precision than the SVM model.


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