Supporting program comprehension using semantic and structural information
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
The Journal of Machine Learning Research
Using information retrieval to support design of incremental change of software
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Software traceability with topic modeling
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Identifying Themes in Social Media and Detecting Sentiments
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
APSEC '10 Proceedings of the 2010 Asia Pacific Software Engineering Conference
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
Clustering Support for Static Concept Location in Source Code
ICPC '11 Proceedings of the 2011 IEEE 19th International Conference on Program Comprehension
Communications of the ACM
Finding the merits and drawbacks of software resources from comments
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Focusing spontaneous feedback to support system evolution
RE '11 Proceedings of the 2011 IEEE 19th International Requirements Engineering Conference
| Hi-index | 0.00 |
User feedback is imperative in improving software quality. In this paper, we explore the rich set of user feedback available for third party mobile applications as a way to extract new/changed requirements for next versions. A potential problem using this data is its volume and the time commitment involved in extracting new/changed requirements. Our goal is to alleviate part of the process through automatic topic extraction. We process user comments to extract the main topics mentioned as well as some sentences representative of those topics. This information can be useful for requirements engineers to revise the requirements for next releases. Our approach relies on adapting information retrieval techniques including topic modeling and evaluating them on different publicly available data sets. Results show that the automatically extracted topics match the manually extracted ones, while also significantly decreasing the manual effort.