Explaining Outcomes of Multi-Party Dialogues using Causal Learning
This work addresses the need for conflict analysis and collaboration design in enterprise settings, but it is incremental as it applies existing causal learning and time series methods to a specific domain.
The paper tackled the problem of explaining sentiment outcomes in multi-party dialogues on enterprise social media by proposing an explainable time series mining algorithm, which uses a decision tree with temporal metrics to predict causes and provides interpretable rules, with experimental results demonstrated on data from a large company.
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.