Analyze and develop a model for sentimental reviews of e-government services using deep learning algorithms with CNN framework

Nowadays the government can introduce many new schemes through online and uploaded in their official web portal. Publics can able to access and avail those facilities on internet by reading news and notifications of such schemes. In order to improve their governance the reviews of the public will be very significant. Since these reviews will help the government to take better decisions. By achieving this, the government may get peoples reviews about such schemes. In the existing system reviews such as manual, oral and somewhere online modes were available for facilitating e-government services. But Artificial Intelligence based techniques like facial recognition and sentimental reviews of the public is not incorporated in the current scenario. So in order to facilitate the government to provide better decision the software based deep learning algorithm called Convolution Neural Networks (CNN) is implemented to analysing the sentimental reviews of e-Government services. In this framework three models were developed to implement a concept of multiple CNN models in which first model can recognize people’s hand written digits, second model can detect sentiments from text sentence which can be given by people about government services, third model can detect sentiment from person face image. This paper involves analyze some of the applications to make the user friendly. In order to make the application more accessible and navigations the well-suited browser, need to be selected. The navigations could be done from one screen to the other in sequence and also help the users to reduce the typing action. Using this method, we can reduce the human interventions in the analysing the reviews and consolidated the result which specifies the overall conclusion of the service.