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Composite Sequential Modeling for Identifying Fake Reviews

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dc.contributor.author Sharma, Yashvardhan
dc.date.accessioned 2024-11-15T09:23:06Z
dc.date.available 2024-11-15T09:23:06Z
dc.date.issued 2018-04
dc.identifier.uri https://www.degruyter.com/document/doi/10.1515/jisys-2017-0501/html
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16394
dc.description.abstract This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques. en_US
dc.language.iso en en_US
dc.publisher De Gruyter en_US
dc.subject Computer Science en_US
dc.subject Spam detection en_US
dc.subject Deep Learning (DL) en_US
dc.subject Machine learning (ML) en_US
dc.subject Fake reviews en_US
dc.title Composite Sequential Modeling for Identifying Fake Reviews en_US
dc.type Article en_US


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