TY - GEN
T1 - Improving Fake Product Detection with Aspect-Based Sentiment Analysis
AU - Li, Jiaming
AU - Fu, Yonghao
AU - Liu, Daoxing
AU - Xu, Ruifeng
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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 ratings 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 in mining the opinion of reviews automatically. In this paper, the goal is to improve fake product detection through text classification technology. Precisely, we use CNN and LSTM models to judge whether the review is quality related or not, which can remove useless reviews, and aspect-based sentiment analysis with an attention mechanism to determine the sentiment polarity of the concerning aspect to get ratings for different aspects. We experiment on the Self-Annotated datasets and results show that by using text classification technology, the performance of fake product detection can be greatly improved.
AB - 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 ratings 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 in mining the opinion of reviews automatically. In this paper, the goal is to improve fake product detection through text classification technology. Precisely, we use CNN and LSTM models to judge whether the review is quality related or not, which can remove useless reviews, and aspect-based sentiment analysis with an attention mechanism to determine the sentiment polarity of the concerning aspect to get ratings for different aspects. We experiment on the Self-Annotated datasets and results show that by using text classification technology, the performance of fake product detection can be greatly improved.
KW - Fake product detection
KW - Natural Language Processing
KW - Text classification
UR - https://www.scopus.com/pages/publications/85091580728
U2 - 10.1007/978-3-030-59585-2_4
DO - 10.1007/978-3-030-59585-2_4
M3 - 会议稿件
AN - SCOPUS:85091580728
SN - 9783030595845
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 49
BT - Cognitive Computing – ICCC 2020 - 4th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Proceedings
A2 - Yang, Yujiu
A2 - Yu, Lei
A2 - Zhang, Liang-Jie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Cognitive Computing, ICCC 2020, held as part of Services Conference Federation, SCF 2020
Y2 - 18 September 2020 through 20 September 2020
ER -