论文标题
从文本消息中检测牙线版本发布事件是否可行？关于堆栈溢出的案例研究
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow
论文作者
论文摘要
主题检测和跟踪（TDT）是文本挖掘区域内的一个非常活跃的研究问题，通常应用于新闻提要和Twitter数据集，并在其中检测到主题和事件。 “事件”的概念很广，但通常适用于可以从单个帖子或消息中检测到的事件。几乎没有引起我们所谓的“微事件”的关注，由于它们的性质，无法从单个文本信息中检测到它们。该研究使用堆栈溢出Q＆A平台和免费/Libre开源软件（Floss）版本从libraries.io DataSet释放了Micro-Event检测对文本数据的可行性。我们使用三个不同的估计器构建用于检测微事件的管道，其参数使用网格搜索方法进行了优化。我们考虑了两个特征空间：通过情感分析的LDA主题建模，以及带有情感分析的HSBM主题。使用交叉验证（RFECV）策略的递归特征消除优化特征空间。 在我们的实验中，我们研究了微事件发生之前或之后的主题分布或情感特征是否存在特征变化，我们彻底评估了分析管道中每种变体的能力以检测微事件。此外，我们对模型进行详细的统计分析，包括影响力案例，方差通胀因素，线性假设的验证，伪R平方措施和无信息率。最后，为了研究微事件检测的极限，我们设计了一种生成具有与现实世界数据相似的微事件合成数据集的方法，并使用它们来识别每个评估的分类器的微事件可检测性阈值。
Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of "event" is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call "micro-events", which, due to their nature, cannot be detected from a single piece of textual information. The study investigates the feasibility of micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform and Free/Libre Open Source Software (FLOSS) version releases from Libraries.io dataset. We build pipelines for detection of micro-events using three different estimators whose parameters are optimized using a grid search approach. We consider two feature spaces: LDA topic modeling with sentiment analysis, and hSBM topics with sentiment analysis. The feature spaces are optimized using the recursive feature elimination with cross validation (RFECV) strategy. In our experiments we investigate whether there is a characteristic change in the topics distribution or sentiment features before or after micro-events take place and we thoroughly evaluate the capacity of each variant of our analysis pipeline to detect micro-events. Additionally, we perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R squared measures and no-information rate. Finally, in order to study limits of micro-event detection, we design a method for generating micro-event synthetic datasets with similar properties to the real-world data, and use them to identify the micro-event detectability threshold for each of the evaluated classifiers.
