Open source software adoption evaluation through feature level sentiment analysis using Twitter data

Abstract

Adopting open source software from the Internet, developers often encounter the problem of accessing the quality of candidate software. To efficiently adopt the system they need a sort of quality guarantee regarding software resources. To assist the developer in software adoption evaluation we have proposed a software adoption assessment approach based on user comments. In our proposed approach, we first collected the textual reviews regarding the software resource, assigned the sentiment polarity (positive or negative) to each comment, extracted the adoption aspect which the comment talks about, and then based on the adoption aspects of the software generated an aggregated sentiment profile of the software. Twitter micro-blogging data about OSS products were crawled, preprocessed, tagged, and then summarized. To evaluate the proposed model, a set of experiments was designed and conducted using different classifiers, i.e. Apriori, GSP, and AdaBoost. For the feature level sentiment summarization we have used Bayesian statistics and frequency distribution techniques. The results show that the proposed approach achieved satisfying precision and recall, i.e. above 80% along with an average accuracy of 70.98%.