A Hybrid Framework Using Natural Language Processing and Collaborative Filtering for Performance Efficient Feedback Mining and Recommendation

Product development insights may be found through user reviews on App stores, product forums, and social media. This feedback is often regarded as the “voice of the users”. This feedback has been subject to a lot of recent research, intending to create systems that can automatically extract, filter, analyze, and report the concerned feedback data in near real time. As per our survey results, often this user feedbacks do not reach the concerned organization promptly due to the volume, veracity, and velocity of feedback from multiple channels. In this rese arch work, we propose using sentiment analysis and social media mining an automatic engine which can be used for better product recommendation and automatic routing of relevant feedback to the product development teams. Our proposed solution is scheduled to run at regular intervals pulling dynamic reviews in an optimized manner with a lesser time complexity and higher efficiency. The reviews are collated from distributed platforms followed by building a domain classification engine on the principles of TF-IDF and Supervised Classifier. This system is used to classify the reviews of the respective enterprises. A sentiment analysis system is built using combined Rule-Based Mining and Supervised Learning Models which makes use of polarity to classify if the feedback is positive or negative. If the polarity is negative, the feedback gets routed to the concerned enterprise for immediate action and if the polarity is positive, it is passed to a user-based collaborative filtering engine which acts as a recommendation system.