Analyzing excessive user feedback: A big data challenge

User involvement in the process of discovering and shaping the product is the base of software systems. In recent years, however, a shift in the user feedback has been observed: repositories of user data have become increasingly more subjected to analysis for improvement purposes. Significant surge has been seen in feedback collected from users in the form of reviews and ratings along with app usage statistics. This led software engineering researchers to deploy big data analytics techniques in order to figure out the requirements that should be met in the future software system releases. While a variety of big data analytics methods exist, it is not clear which ones have been used and what are the benefits and disadvantages of these proposals. In this paper, we have aimed to outline the recently published proposals for big data analytics techniques for user feedback analysis. We found that the majority of the techniques rest on natural language processing concepts and visualization. Our findings also indicate that the majority of the proposals come from the United States, Germany and the United Kingdom. Moreover, we also found the proposed techniques perform well with the chosen datasets however the generalizability and scalability of these method raised concerns as these methods are not evaluated based on real-world cases.