Sentiment Text Analysis for Providing Individualized Feed of Recommendations Using Reinforcement Learning

We present an implementation of a new method of producing personalized text recommendations to users based on their sentiment. This method aims to identify patterns in user behavior based on a reward that results from each actual application in which the algorithm is used. The system consists of a semantic analysis algorithm that combines clustering and word processing, a level of neural networks designed to predict user reward for each text, and a level of algorithms that attempt to explore the problem area to adapt to changes in user behavior. This research work combines many known methods as well as variations thereof into a single system which in addition has the technical specifications to be used in typical applications like newsfeeds.