Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. The outcome of a conversation may be positive or negative. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis as well as future collaboration design. We propose an explainable time series mining algorithm for such analysis. A dialogue is represented as an attributed time series of occurrences of keywords, EMPATH categories, and inferred sentiments at various points in its progress. A special decision tree, with decision metrics that take into account temporal relationships between dialogue events, is used for predicting the cause of the outcome sentiment. Interpretable rules mined from the classifier are used to explain the prediction. Experimental results are presented for the enterprise social media posts in a large company.

中文翻译：

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使用因果学习解释多方对话的结果

在企业社交媒体中，关于技术以及非技术主题的多方对话都很常见。对话的结果可能是正面的，也可能是负面的。从冲突分析以及未来的协作设计的角度分析对话为何以特定的情感结束是很重要的。我们提出了一种可解释的时间序列挖掘算法来进行此类分析。对话表示为在其进行中的各个时间点出现的关键字，EMPATH类别和推断的情感的归因时间序列。带有考虑了对话事件之间的时间关系的决策度量的特殊决策树用于预测结果情感的原因。从分类器中提取的可解释规则用于解释预测。