Open AccessAlgorithm for extracting product feature from e-commerce comment
Author(s) -
Chanida Kaewphet,
Nawaporn Wisitpongpun
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v22.i2.pp1199-1207
Subject(s) - syntax , product (mathematics) , computer science , feature (linguistics) , purchasing , e commerce , phone , feature extraction , information retrieval , natural language processing , artificial intelligence , data mining , world wide web , marketing , mathematics , linguistics , business , philosophy , geometry
Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.