(ICCC 2020) Improve Fake Products Detection with Aspect-based Sentiment Analysis

Abstract: With the development of e-commerce, the number of counterfeit products is increasing and the rights and interests of customers have been seriously infringed. A product can be evaluated by reviews and rating objectively. However, the topics of reviews are diverse while customers tend to focus on only a few aspects, and many reviews have wrong scores that are inconsistent with the content. Natural language processing (NLP) is helpful to mining the opinion of reviews automatically. In this paper, the goal is to to improve fake products detection through text classification technology. Precisely, we use CNN and LSTM model to judge whether the review is quality related or not, which can remove useless reviews, and aspect-based sentiment analysis with attention mechanism to determine the sentiment polarity of the concerned aspect to get ratings for different aspects. We experiment on the Self-Annotated dataset and results show that by using text classification technology, the performance of fake product detection can be greatly improved.

Authors: Jiaming Li (Harbin Institute of Technology, Shenzhen & South China University of Technology, China); Yonghao Fu (Harbin Institude of Technology(Shen Zhen), China); Daoxing Liu (Harbin Institute of Technology at Shenzhen, China); Ruifeng Xu (Harbin Institute of Technology, Shenzhen, China)

Email: lijm_hitsz@163.com, 18340852925@163.com, daoxingliu@163.com, xuruifeng@hit.edu.cn