Embedded Deep NN with Convolution NN-ID to Binominal Sentiment Prediction

Objective: The objective is to develop a generalized deep learning system for sentiment analysis in a feedback system. This implies an intention to create a model that can effectively analyze sentiment across different domains or datasets. Methodology: It is proven that the deep learning systems are playing a major role in NLP, but still there is a need of enhancement to make the prediction system a perfect one. The Convolution Neural Network (CNN) could emphasize its ability to capture spatial dependencies in textual data and the Deep Neural Network(DNN) fetches the feature in text with varying semantics. In this context the proposed work Embed the Deep Neural Network with Convolution Neural Network in a three-layered ID architecture(EDNN with CNN-ID.) The model is enhanced in generalizing input_shape, kernel, channel, and filter parameters of the convolution layer. It produces remarkable results in sentimental analysis. The work is experimented with a feedback system from three different domains like Movie Review, Doctor Treatment Review, Product Review. Result: The EDNN with CNN-ID produces accuracy 93%, 91% and 91% for the three datasets