Software development encompasses many collaborative tasks in which usually several persons are involved. Close collaboration and the synchronization of different members of the development team require effective communication. One established communication channel are meetings which are, however, often not as effective as expected. Several approaches already focused on the analysis of meetings to determine the reasons for inefficiency and dissatisfying meeting outcomes. In addition to meetings, text-based communication channels such as chats and e-mails are frequently used in development teams. Communication via these channels requires a similar appropriate behavior as in meetings to achieve a satisfying and expedient collaboration. However, these channels have not yet been extensively examined in research. In this paper, we present an approach for analyzing interpersonal behavior in text-based communication concerning the conversational tone, the familiarity of sender and receiver, the sender's emotionality, and the appropriateness of the used language. We evaluate our approach in an industrial case study based on 1947 messages sent in a group chat in Zulip over 5.5 months. Using our approach, it was possible to automatically classify written sentences as positive, neutral, or negative with an average accuracy of 62.97% compared to human ratings. Despite this coarse-grained classification, it is possible to gain an overall picture of the adequacy of the textual communication and tendencies in the group mood.

中文翻译：

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通过使用机器学习分析聊天中基于文本的交流来识别软件开发团队的情绪

软件开发包含许多协作任务，通常涉及几个人。开发团队不同成员的密切协作和同步需要有效的沟通。一种已建立的沟通渠道是会议，但通常不如预期有效。一些方法已经侧重于对会议进行分析，以确定效率低下和会议结果不满意的原因。除了会议之外，开发团队还经常使用基于文本的沟通渠道，例如聊天和电子邮件。通过这些渠道进行的沟通需要与会议中类似的适当行为，以实现令人满意和方便的协作。然而，这些渠道尚未在研究中得到广泛研究。在本文中，我们提出了一种分析基于文本的交流中的人际行为的方法，涉及对话语气、发送者和接收者的熟悉程度、发送者的情绪以及所用语言的适当性。我们根据 5.5 个月内在 Zulip 的群聊中发送的 1947 条消息，在工业案例研究中评估了我们的方法。使用我们的方法，可以将书面句子自动分类为正面、中性或负面，与人类评分相比，平均准确率为 62.97%。尽管有这种粗粒度的分类，还是有可能全面了解文本交流的充分性和群体情绪的倾向。的情感，以及所用语言的适当性。我们根据 5.5 个月内在 Zulip 的群聊中发送的 1947 条消息，在工业案例研究中评估了我们的方法。使用我们的方法，可以将书面句子自动分类为正面、中性或负面，与人类评分相比，平均准确率为 62.97%。尽管有这种粗粒度的分类，还是有可能全面了解文本交流的充分性和群体情绪的倾向。的情感，以及所用语言的适当性。我们在一个工业案例研究中评估了我们的方法，该研究基于 5.5 个月内在 Zulip 的群聊中发送的 1947 条消息。使用我们的方法，可以将书面句子自动分类为正面、中性或负面，与人类评分相比，平均准确率为 62.97%。尽管有这种粗粒度的分类，还是有可能全面了解文本交流的充分性和群体情绪的倾向。97% 与人类评分相比。尽管有这种粗粒度的分类，还是有可能全面了解文本交流的充分性和群体情绪的倾向。97% 与人类评分相比。尽管有这种粗粒度的分类，还是有可能全面了解文本交流的充分性和群体情绪的倾向。