  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.
