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A Taxonomy for User Feedback Classifications

: Santos, Rubens; Groen, Eduard C.; Villela, Karina

Fulltext ()

Spoletini, P.:
REFSQ-2019. REFSQ Workshops, Doctoral Symposium, Live Studies Track, and Poster Track. Online resource : Joint Proceedings of REFSQ-2019 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 25th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2019), Essen, Germany, March 18th, 2019
La Clusaz: CEUR, 2019 (CEUR Workshop Proceedings 2376)
Art. 10, 10 pp.
International Conference on Requirements Engineering - Foundation for Software Quality (REFSQ) <25, 2019, Essen>
Conference Paper, Electronic Publication
Fraunhofer IESE ()
Computer software selection and evaluation; Requirements engineering; Taxonomies

Online user feedback contains information that is of interest to requirements engineering (RE). Natural language processing (NLP) techniques, especially classification algorithms, are a popular way of automatically classifying requirements-relevant contents. Research into this use of NLP in RE has sought to answer different research questions, often causing their classifications to be incompatible. Identifying and structuring these classifications is therefore urgently needed. We present a preliminary taxonomy that we constructed based on the findings from a systematic literature review, which places 78 classifications categories for user feedback into four groups: Sentiment, Intention, User Experience, and Topic. The taxonomy reveals the purposes for which user feedback is analyzed in RE, provides an initial harmonization of the vocabulary in this research area, and may inspire researchers to investigate classifications they had previously not considered. This paper intends to foster discussions among NLP experts and to identify further improvements to the taxonomy.