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Sentiment Analysis using machine learning algorithms: online women clothing reviews

Xie, Shuangyin (2019) Sentiment Analysis using machine learning algorithms: online women clothing reviews. Masters thesis, Dublin, National College of Ireland.

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Abstract

Internet technology has been closely related to life. It not only convenient people's lives but also allows people to share information, especially in the field of e-commence. People leave message and share their feelings online. As a result, sentiment analysis becomes more and more attracted. Accurate sentiment analysis not only allows customers to better understand the product, but also enables the company to get better feedback from the market. In this paper, we use data set from online women clothing reviews to conduct sentiment analysis, which can be downloaded from Kaggle. The machine learning methods used in this research are Support Vector Machine, Logistic Regression, Random Forest, Naive Bayes. All experiments were done in this research using python. We evaluate the model in terms of accuracy, precision, recall, F1-score and Area Under Curve(AUC). This study provides us with sentimental analysis of various women clothing opinions dividing them Positive, Negative and Neutral behaviour. These data suggest that the Naive Bayes gives highest accuracy to classify the Reviews, which is 93%.
Keywords: sentiment analysis, machine learning, Support Vector Machine, Logistic Regression, Random Forest, Naive Bayes.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Online Shopping
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Online Shopping
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 10 Jun 2020 15:28
Last Modified: 10 Jun 2020 15:28
URI: http://trap.ncirl.ie/id/eprint/4265

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