Back to Search
Start Over
A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network
- Source :
- Journal of Computational and Theoretical Nanoscience. 17:3421-3426
- Publication Year :
- 2020
- Publisher :
- American Scientific Publishers, 2020.
-
Abstract
- Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.
- Subjects :
- Computational Mathematics
Long short term memory
Recurrent neural network
Computer science
Speech recognition
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
General Materials Science
02 engineering and technology
General Chemistry
Electrical and Electronic Engineering
Condensed Matter Physics
Subjects
Details
- ISSN :
- 15461955
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Journal of Computational and Theoretical Nanoscience
- Accession number :
- edsair.doi...........b474fdcd8529cf35fb28c0459112616d