Real-Time Sentiment Analysis of Customer Feedback Using Neural Networks and NLP Methods

Realistic sentiment analysis will be in need of understanding customer feedbacks system's, facilitating data-depending decision making and improving user experience. This research model utilizing neural based networks and natural language processing systems (NLP) mechanism to realize faster and accurate level of customer sentiment computation on textual data. The proposed strategy investigates state-of-the-art neural network infrastructure to learn useful data-patterns and sentiments from the raw data. On other hand NLP performs factitive in pre-processing, feature extraction and textual-context awareness in interpretation of text. By enabling realistic processing of customer feedbacks, the system gives immediate visibility into customers' preference with their complaints and satisfaction grade. It also show that supervisor learning models in conjunction with NLP will effectively handles the heterogeneity as well as complexity in customer feedbacks. Finally, the proposed system demonstrate the way of real-time sentiment analysis ability can improve customer engagement leading to data-depend business mechanism.