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Amazon product sentiment analysis using RapidMiner

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Main Authors: Muhammad Firdaus, Mustapha, Nur Hasifah, A Razak, Nur Amirah, Marzuki, Nur Saidatul Sa’adiah, Tajul Othamany
Other Authors: mdfirdaus@uitm.edu.my
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2023
Subjects:
Decision tree
Naive bayes
Random forest
Sentiment analysis
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77661
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spelling my.unimap-776612023-01-12T04:00:53Z Amazon product sentiment analysis using RapidMiner Muhammad Firdaus, Mustapha Nur Hasifah, A Razak Nur Amirah, Marzuki Nur Saidatul Sa’adiah, Tajul Othamany Muhammad Firdaus, Mustapha mdfirdaus@uitm.edu.my Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Cawangan Kelantan Decision tree Naive bayes Random forest Sentiment analysis Link to publisher's homepage at https://amci.unimap.edu.my/ Nowadays, online reviews from customers have created significance for any business especially when it comes to Amazon website. This research predicts the customer reviews based on three main categories; health and beauty, toys and games and electronics. The reviews are classified whether as positive, negative, or neutral. Sentiment Analysis is a data analysis concept in which a collection of reviews is considered, and those reviews are analyzed, processed, and recommended to the user. The dataset use in this research is collected from the Dataworld website. The research presented in this paper was carried out initially; the reviews must be pre-processed in order to remove the unwanted data before being converted from text to vector representation using a range of feature extraction techniques such as TF-IDF. After that, the dataset is classified using Naive Bayes, Decision Tree and Random Forest algorithms. The accuracy, precision and recall were implemented as performance measures in order to evaluate the performance sentiment classification for the given reviews. The result shows that Decision Tree is the best classifier with the highest accuracy for the health and beauty, and electronic categories. For the toys and games category, the best classifier with the highest accuracy is Random Forest. 2023-01-12T04:00:52Z 2023-01-12T04:00:52Z 2022-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 336-349 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77661 https://amci.unimap.edu.my/ en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Decision tree
Naive bayes
Random forest
Sentiment analysis
spellingShingle Decision tree
Naive bayes
Random forest
Sentiment analysis
Muhammad Firdaus, Mustapha
Nur Hasifah, A Razak
Nur Amirah, Marzuki
Nur Saidatul Sa’adiah, Tajul Othamany
Muhammad Firdaus, Mustapha
Amazon product sentiment analysis using RapidMiner
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 mdfirdaus@uitm.edu.my
author_facet mdfirdaus@uitm.edu.my
Muhammad Firdaus, Mustapha
Nur Hasifah, A Razak
Nur Amirah, Marzuki
Nur Saidatul Sa’adiah, Tajul Othamany
Muhammad Firdaus, Mustapha
format Article
author Muhammad Firdaus, Mustapha
Nur Hasifah, A Razak
Nur Amirah, Marzuki
Nur Saidatul Sa’adiah, Tajul Othamany
Muhammad Firdaus, Mustapha
author_sort Muhammad Firdaus, Mustapha
title Amazon product sentiment analysis using RapidMiner
title_short Amazon product sentiment analysis using RapidMiner
title_full Amazon product sentiment analysis using RapidMiner
title_fullStr Amazon product sentiment analysis using RapidMiner
title_full_unstemmed Amazon product sentiment analysis using RapidMiner
title_sort amazon product sentiment analysis using rapidminer
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2023
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77661
_version_ 1772813094956826624
score 13.211869

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