Analysis of Online Marketplace Data on Social Networks Using LSTM
- Resource Type
- Conference
- Authors
- Prova, Alpana Akhi; Akter, Tania; Islam, Md Rafiqul; Uddin, Md Rakib; Hossain, Tanvir; Hannan, Md; Hossain, Md Safaet
- Source
- 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) Advances in Electrical Engineering (ICAEE), 2019 5th International Conference on. :381-385 Sep, 2019
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Long Short-Term Memory (LSTM)
Online Marketplace
Recurrent Neural Network (RNN)
- Language
- ISSN
- 2378-2692
In recent years, the users of the online marketplace on social networks are increased rapidly. The availability of online marketplace data has been created a great research scope for analyzing. The main concern of this paper is to understand and recognize the evolving characteristics of online marketplace real user’s behavior on the social network. We used ‘Facepager’ as an effective tool to capture the data from Facebook and applied LSTM Neural Network (NN) to classify positive and negative review and observe better accuracy. Our future work is to conduct an explainable attention-based LSTM to detect anomalous semantic information from social network data.