loading subjects...
This paper aims at demonstrating non-functional requirements analysis requirements analysis normally used supervised methods which need a lot of manual annotation work. Using unsupervised approaches to classify non-functional requirements can save a lot of labor and time, but the accuracy of the existing approaches is relatively low. In order to solve the dilemma, we propose a new clustering approach in this paper. The approach is an improved version of the previous aspect segmentation approach, but differs in terms of classification strategy, the representation of the review sentences, and the strategy for selecting new keywords. Experiments are conducted and compared on a software reviews dataset. Results show an improved performance of the new approach.