Next Article in Journal
Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer
Next Article in Special Issue
Classical Music Specific Mood Automatic Recognition Model Proposal
Previous Article in Journal
Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches
Previous Article in Special Issue
A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network
Article

Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context

1
Department of Data Informatics, (National) Korea Maritime and Ocean University, Busan 49112, Korea
2
Department of Data Science, (National) Korea Maritime and Ocean University, Busan 49112, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Amir H. Gandomi
Electronics 2021, 10(20), 2481; https://doi.org/10.3390/electronics10202481
Received: 13 August 2021 / Revised: 30 September 2021 / Accepted: 7 October 2021 / Published: 12 October 2021
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare Volume II)
Product reviews become more important in the buying decision-making process of customers. Exploiting and analyzing customer product reviews in sentiments also become an advantage for businesses and researchers in e-commerce platforms. This study proposes a sentiment evaluation model of customer reviews by extracting objects, emotional words for emotional level analysis, using machine learning algorithms. The research object is the Vietnamese language, which has special semantic structures and characteristics. In this research model, emotional dictionaries and sets of extract rules are combined to build a data training data set based on the semantic dependency relationship between words in sentences of the given Vietnamese context. The recurrent neural network model (RNN) solves the emotional analysis issue, specifically, the long short-term memory neural network (LSTMs). This analysis model combines the vector representations of words with a continuous bag-of-words (CBOW) architecture. Our system is designed to crawl realistic data in an e-commerce website and automatically aggregate them. These data will be stored in MongoDB before processing and input into our model on the server. Then, the system can exploit the features in products reviews and classify customer reviews. These features extracted from different feedback on each shopping step and depending on the kinds of products. Finally, there is a web-app to connect to a server and visualize all the research results. Based on the research results, enterprises can follow up their customers in real-time and receive recommendations to understand their customers. From there, they can improve their services and provide sustainable consumer service. View Full-Text
Keywords: Natural Language Processing; e-commerce website; product reviews; Vietnamese; sentiment analysis; text classification; big data; application Natural Language Processing; e-commerce website; product reviews; Vietnamese; sentiment analysis; text classification; big data; application
Show Figures

Figure 1

MDPI and ACS Style

Le, N.-B.-V.; Huh, J.-H. Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context. Electronics 2021, 10, 2481. https://doi.org/10.3390/electronics10202481

AMA Style

Le N-B-V, Huh J-H. Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context. Electronics. 2021; 10(20):2481. https://doi.org/10.3390/electronics10202481

Chicago/Turabian Style

Le, Ngoc-Bao-Van, and Jun-Ho Huh. 2021. "Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context" Electronics 10, no. 20: 2481. https://doi.org/10.3390/electronics10202481

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop