Eat it, Review it: A New Approach for Review Prediction


EOI: 10.11242/viva-tech.01.02.09

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Citation

Deepal S. Thakur, Rajiv N. Tarsarya, Ashwini Save, "Eat it, Review it: A New Approach for Review Prediction", VIVA-IJRI Volume 1, Issue 2, Article 9, pp. 1-6, 2019. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays, Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely adopted by organizations around the world. A basic task in deep learning is classification be it image or text. Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis. Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis. Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system focuses on context based review prediction and will provide full length sentence. This will help to write a proper reviews by understanding the context of user.

Keywords

CNN, Deep Learning, LSTM, Machine Learning, RCNN, RNN.

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