(University of Economics and Law, Ho Chi Minh City, Vietnam; Viet Nam National University, Ho Chi Minh City,, Viet Nam)(2) Hien Minh Bui
(UEH College of Technology and Design (UEH-CTD), University of Economics Ho Chi Minh City (UEH), Vietnam, Viet Nam)(3) Phung Kim Thai
(UEH College of Technology and Design (UEH-CTD), University of Economics Ho Chi Minh City (UEH), Vietnam, Viet Nam)*corresponding author
AbstractFeedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage.
Keywordscustomer feedback; sentiment analysis; mobile commerce; machine and deep learning; POS tagging; aspect extraction.
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DOIhttps://doi.org/10.26555/ijain.v9i2.976 |
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