Improving Fake Product Detection with Aspect-Based Sentiment Analysis
- 14 September 2020
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 16 references indexed in Scilit:
- Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word EmbeddingsPublished by Association for Computational Linguistics (ACL) ,2018
- Detection of fake opinions using time seriesExpert Systems with Applications, 2016
- Fake Review Detection From a Product Review Using Modified Method of Iterative Computation FrameworkMATEC Web of Conferences, 2016
- Attention-based LSTM for Aspect-level Sentiment ClassificationPublished by Association for Computational Linguistics (ACL) ,2016
- Effective Approaches to Attention-based Neural Machine TranslationPublished by Association for Computational Linguistics (ACL) ,2015
- Reviews without a Purchase: Low Ratings, Loyal Customers, and DeceptionJournal of Marketing Research, 2014
- Convolutional Neural Networks for Sentence ClassificationPublished by Association for Computational Linguistics (ACL) ,2014
- A Research of Taobao Cheater DetectionIFIP Advances in Information and Communication Technology, 2014
- Extensions of recurrent neural network language modelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Sentiment analysisPublished by Association for Computing Machinery (ACM) ,2003