2015/09/18 by Issa Atoum, Atoum, Issa, Chih How Bong +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Software Engineering (cs.SE) #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1509.05736
openalex publication_date 2015/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Prepared domain specific datasets plays an important role to supervised learning approaches. In this article a new sentence dataset for software quality-in-use is proposed. Three experts were chosen to annotate the data using a proposed annotation scheme. Then the data were reconciled in a (no match eliminate) process to reduce bias. The Kappa, k statistics revealed an acceptable level of agreement; moderate to substantial agreement between the experts. The built data can be used to evaluate software quality-in-use models in sentiment analysis models. Moreover, the annotation scheme can be used to extend the current dataset.