User review analysis for requirement elicitation

Online reviews are an important channel for requirement elicitation. However, requirement engineers face challenges when analysing online user reviews, such as data volumes, technical supports, existing techniques, and legal barriers. This thesis proposes a framework solving user review analysis problems for the purpose of requirement elicitation that sets up a channel from downloading user reviews to structured analysis data. This framework is believed to be able to solve the problems because (a) the structure of this framework is composed of several loosely integrated components, which not only realize the flow of data from downloading raw user reviews to the structured analysis results, but also provide adaptability and flexibility for wider future applications; (b) the reasonable use of linguistic rules makes it possible to adjust and control the internal details of the system in this data flow; (c) natural language processing (NLP) technologies, such as chunking, regular expressions, and especially Stanford dependency trees, provide substantial technical support for this framework. Three mobile app user review datasets were used to evaluate the functionalities. 6081 user reviews from the first dataset is used for the development of linguistic rules. The first two datasets are used to enrich the popular opinions and the keywords list. The third dataset acts as a control group. The performance results of the prototype demonstrate that this framework is practical and usable. The main contributions of this work are: (1) this thesis proposed a framework to solve the user review analysis problem for requirement elicitation; (2) the prototype of this framework proves its feasibility; (3) the experiments prove the effectiveness and efficiency of this framework.