Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews
Open Access
- 8 May 2021
- journal article
- research article
- Published by Hindawi Limited in Mathematical Problems in Engineering
- Vol. 2021, 1-10
- https://doi.org/10.1155/2021/5536560
Abstract
Consumer feedback is highly valuable in business to assess their performance and is also beneficial to customers as it gives them an idea of what to expect from new products. In this research, the aim is to evaluate different deep learning approaches to accurately predict the opinion of customers based on mobile phone reviews obtained from Amazon.com. The prediction is based on analysing these reviews and categorizing them as positive, negative, or neutral. Different deep learning algorithms have been implemented and evaluated such as simple RNN with its four variants, namely, Long Short-Term Memory Networks (LRNN), Group Long Short-Term Memory Networks (GLRNN), gated recurrent unit (GRNN), and update recurrent unit (UGRNN). All evaluated algorithms are combined with word embedding as feature extraction approach for sentiment analysis including Glove, word2vec, and FastText by Skip-grams. The five different algorithms with the three feature extraction methods are evaluated based on accuracy, recall, precision, and F1-score for both balanced and unbalanced datasets. For the unbalanced dataset, it was found that the GLRNN algorithms with FastText feature extraction scored the highest accuracy of 93.75. This result achieved the highest accuracy on this dataset when compared with other methods mentioned in the literature. For the balanced dataset, the highest achieved accuracy was 88.39 by the LRNN algorithm.Keywords
Funding Information
- Taif University (TURSP-2020/211)
This publication has 10 references indexed in Scilit:
- Sentiment Analysis of Comment Texts Based on BiLSTMIEEE Access, 2019
- Sentiment Analysis of Amazon Products Using Ensemble Machine Learning AlgorithmInternational Journal of Mathematical, Engineering and Management Sciences, 2019
- Extracting Customer Reviews from Online Shopping and Its Perspective on Product DesignVietnam Journal of Computer Science, 2019
- SVM and k-Means Hybrid Method for Textual Data Sentiment AnalysisBaltic Journal of Modern Computing, 2019
- SVM and k-Means Hybrid Method for Textual Data Sentiment AnalysisBaltic Journal of Modern Computing, 2019
- Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction MethodInternational Journal of Interactive Multimedia and Artificial Intelligence, 2019
- Sentiment classification of online consumer reviews using word vector representationsProcedia Computer Science, 2018
- Comparative Study of Machine Learning Approaches for Amazon ReviewsProcedia Computer Science, 2018
- Enriching Word Vectors with Subword InformationTransactions of the Association for Computational Linguistics, 2017
- Classification of Sentimental Reviews Using Machine Learning TechniquesProcedia Computer Science, 2015