Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis

Márcio Guia, Rodrigo Rocha Silva, Jorge Bernardino

2019

Abstract

Every day, we deal with a lot of information on the Internet. This information can have origin from many different places such as online review sites and social networks. In the midst of this messy data, arises the opportunity to understand the subjective opinion about a text, in particular, the polarity. Sentiment Analysis and Text Classification helps to extract precious information about data and assigning a text into one or more target categories according to its content. This paper proposes a comparison between four of the most popular Text Classification Algorithms - Naive Bayes, Support Vector Machine, Decision Trees and Random Forest - based on the Amazon Unlocked mobile phone reviews dataset. Moreover, we also study the impact of some attributes (Brand and Price) on the polarity of the review. Our results demonstrate that the Support Vector Machine is the most complete algorithm of this study and achieve the highest values in all the metrics such as accuracy, precision, recall, and F1 score.

Paper Citation

in Harvard Style

Guia M., Silva R. and Bernardino J. (2019). Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 525-531. DOI: 10.5220/0008364105250531

in Bibtex Style

@conference{kdir19,
author={Márcio Guia and Rodrigo Rocha Silva and Jorge Bernardino},
title={Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={525-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008364105250531},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis
SN - 978-989-758-382-7
AU - Guia M.
AU - Silva R.
AU - Bernardino J.
PY - 2019
SP - 525
EP - 531
DO - 10.5220/0008364105250531
PB - SciTePress