Related papers: Explaining Outcomes of Multi-Party Dialogues using Causal Learning

Explaining Outcomes of Multi-Party Dialogues using Causal Learning

URL: http://arxiv.org/abs/2105.00944v1
Date: Mon, 3 May 2021 15:18:53 GMT
Title: Explaining Outcomes of Multi-Party Dialogues using Causal Learning
Authors: Priyanka Sinha, Pabitra Mitra, Antonio Anastasio Bruto da Costa, Nikolaos Kekatos
Abstract summary: Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis. We propose an explainable time series mining algorithm for such analysis.
Score: 6.212955085775758
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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