Social aspects in software development teams are of particular importance for a successful project closure. To analyze sentiments in software projects, there are several tools and approaches available. These tools analyze text-based communication based on the used words to predict whether they appear to be positive, negative, or neutral for the receiver of the message. In the research project ComContA, we investigate so-called sentiment analysis striving to analyze the content of text-based communication in development teams with regard to the statement's polarity. That is, we analyze whether the communication appears to be adequate (i.e., positive or neutral) or negative. In a workshop paper, we presented a tool called SEnti-Analyzer that allows to apply sentiment analysis to verbal communication in meetings of software projects. In this technical report, we present the extended functionalities of the SEnti-Analyzer by also allowing the analysis of text-based communication, we improve the prediction of the tool by including established sentiment analysis tools, and we evaluate the tool with respect to its accuracy. We evaluate the tool by comparing the prediction of the SEnti-Analyzer to pre-labeled established data sets used for sentiment analysis in software engineering and to perceptions of computer scientists. Our results indicate that in almost all cases at least two of the three votes coincide, but in only about half of the cases all three votes coincide. Our results raise the question of the "ultimate truth" of sentiment analysis outcomes: What do we want to predict with sentiment analysis tools? The pre-defined labels of established data sets? The perception of computer scientists? Or the perception of single computer scientists which appears to be the most meaningful objective?
翻译:软件开发团队的社会层面对于成功关闭项目特别重要。 为了分析软件项目中的情绪, 我们有一些工具和方法。 这些工具分析基于文本的通信, 以用来预测信息接收者是否正、 负或中。 在ComContA的研究项目中, 我们调查所谓的情绪分析, 试图分析开发团队中基于文本的通信内容, 分析声明的极性。 也就是说, 我们分析通信是否足够( 积极或中性) 或消极。 在一份研讨会论文中, 我们展示了一个名为 SENti- Analyzer 的工具, 用于分析文字通信是否对接收者来说是正面、 负面或中性。 在这份技术报告中, 我们展示了SENti- Anazer 的扩大功能, 试图分析发展团队中基于文本的通信内容。 我们通过包含既定的情绪分析工具来改进对工具的预测。 我们通过比较SENti- Anazer的预测来评估工具的准确性。 我们通过将SENti- Anazer 的预感官 来将情绪分析应用到软件项目会议中最起码的情绪分析 。 在计算机分析中, 的正确的数据分析中, 显示我们三个分析结果中, 显示的所有数据序列分析结果的准确性分析结果, 似乎似乎似乎只有一半。