TELKOMNIKA Telecommunication Computing Electronics and Control
Amazon products reviews classification based on machine
learning, deep learning methods and BERT
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
Amazon products reviews classification based on machine
learning, deep learning methods and BERT
Amazon products reviews classification based on machine
learning, deep learning methods and BERT
Subject
Deep learning
Feature extraction
Machine learning
Sentiment analysis
Transformer technique
Feature extraction
Machine learning
Sentiment analysis
Transformer technique
Description
In recent times, the trend of online shopping through e-commerce stores and
websites has grown to a huge extent. Whenever a product is purchased on an
e-commerce platform, people leave their reviews about the product. These
reviews are very helpful for the store owners and the product’s manufacturers
for the betterment of their work process as well as product quality. An
automated system is proposed in this work that operates on two datasets D1
and D2 obtained from Amazon. After certain preprocessing steps, N-gram and
word embedding-based features are extracted using term frequency-inverse
document frequency (TF-IDF), bag of words (BoW) and global vectors
(GloVe), and Word2vec, respectively. Four machine learning (ML) models
support vector machines (SVM), logistic regression (RF), logistic regression
(LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models
convolutional neural network (CNN), long-short term memory (LSTM), and
standalone bidirectional encoder representations (BERT) are used to classify
reviews as either positive or negative. The results obtained by the standard
ML, DL models and BERT are evaluated using certain performance
evaluation measures. BERT turns out to be the best-performing model in the
case of D1 with an accuracy of 90% on features derived by word embedding
models while the CNN provides the best accuracy of 97% upon word
embedding features in the case of D2. The proposed model shows better
overall performance on D2 as compared to D1.
websites has grown to a huge extent. Whenever a product is purchased on an
e-commerce platform, people leave their reviews about the product. These
reviews are very helpful for the store owners and the product’s manufacturers
for the betterment of their work process as well as product quality. An
automated system is proposed in this work that operates on two datasets D1
and D2 obtained from Amazon. After certain preprocessing steps, N-gram and
word embedding-based features are extracted using term frequency-inverse
document frequency (TF-IDF), bag of words (BoW) and global vectors
(GloVe), and Word2vec, respectively. Four machine learning (ML) models
support vector machines (SVM), logistic regression (RF), logistic regression
(LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models
convolutional neural network (CNN), long-short term memory (LSTM), and
standalone bidirectional encoder representations (BERT) are used to classify
reviews as either positive or negative. The results obtained by the standard
ML, DL models and BERT are evaluated using certain performance
evaluation measures. BERT turns out to be the best-performing model in the
case of D1 with an accuracy of 90% on features derived by word embedding
models while the CNN provides the best accuracy of 97% upon word
embedding features in the case of D2. The proposed model shows better
overall performance on D2 as compared to D1.
Creator
Saman Iftikhar, Bandar Alluhaybi, Mohammed Suliman, Ammar Saeed, Kiran Fatima
Source
http://telkomnika.uad.ac.id
Date
Feb 16, 2023
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Saman Iftikhar, Bandar Alluhaybi, Mohammed Suliman, Ammar Saeed, Kiran Fatima, “TELKOMNIKA Telecommunication Computing Electronics and Control
Amazon products reviews classification based on machine
learning, deep learning methods and BERT,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4595.
Amazon products reviews classification based on machine
learning, deep learning methods and BERT,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4595.