论文标题
使用情感对计算机视觉服务问题进行排名
Ranking Computer Vision Service Issues using Emotion
论文作者
论文摘要
软件开发人员越来越多地使用机器学习API来实现“智能”功能。研究表明，将机器学习纳入应用程序会增加技术债务，创建数据依赖性，并因非确定性行为引起不确定性。但是，我们对处理此类问题的软件开发人员的情绪状态知之甚少。在本文中，我们对1,245个有关计算机视觉API的堆栈溢出文章中发现的情感进行了景观分析。我们调查了现有的情感分类器情感的应用，并手动验证我们的结果。我们发现情绪曲线因不同的问题类别而有所不同。
Software developers are increasingly using machine learning APIs to implement 'intelligent' features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to non-deterministic behaviour. However, we know very little about the emotional state of software developers who deal with such issues. In this paper, we do a landscape analysis of emotion found in 1,245 Stack Overflow posts about computer vision APIs. We investigate the application of an existing emotion classifier EmoTxt and manually verify our results. We found that the emotion profile varies for different question categories.
