%0 Journal Article
%T A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods
%A Jessica Kubrusly
%A Ana Luiza Neves
%A Thamires Louzada Marques
%J Open Journal of Statistics
%P 357-372
%@ 2161-7198
%D 2022
%I Scientific Research Publishing
%R 10.4236/ojs.2022.123023
%X With the
increasing interest in e-commerce shopping, customer reviews</span><span style=\"font-family:Verdana;\"> have</span> <span style=\"font-family:Verdana;\">become
one of the most important elements that determine customer satisfaction
regarding products. This demonstrates the importance of working</span> <span style=\"font-family:Verdana;\">with Text Mining. This study is based on The Women</span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">กฏ</span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">s Clothing E-Com</span></span></span><span><span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\">merce Reviews database, which consists of reviews written by real
customers. The aim of this paper is to conduct a Text Mining approach on a set
of customer reviews. Each review was classified as either a positive or
negative re</span><span style=\"font-family:Verdana;\">view by employing a
classification method. Four tree-based methods were ap</span><span style=\"font-family:Verdana;\">plied to solve the
classification problem, namely Classification Tree, Random Forest, Gradient
Boosting and XGBoost. The dataset was categorized into training and test sets.
The results indicate that the Random Forest method displays an overfitting,
XGBoost displays an overfitting if the number of trees is too high,
Classification Tree is good at detecting negative reviews and bad at detecting
positive reviews and the Gradient Boosting shows stable values and quality
measures above 77% for the test dataset. A consensus between the applied
methods is noted for important classification terms.
%K Text Mining
%K Supervised Classification
%K Tree-Based Methods
%K Classification Trees
%K Random Forest
%K Gradient Boosting
%K XGBoost
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=117776