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- W2984057763 abstract "Human-coding reliant conversation analysis methods are ineffective when analyzing large volumes of data. In this paper, we propose a text analytics framework for automated conversation pattern analysis. This framework first extracts speech acts (i.e., activities) from conversation logs, and then analyzes their flow through frequent pattern mining algorithms to reveal insightful communication patterns. Using a real-world data set collected from a customer service center, we demonstrate the usefulness of the framework for identifying patterns that are associated with service quality outcomes. Our work has implications for the design of communication policies and systems for customer service management." @default.
- W2984057763 created "2019-11-22" @default.
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- W2984057763 date "2020-06-01" @default.
- W2984057763 modified "2023-09-24" @default.
- W2984057763 title "A text analytics framework for automated communication pattern analysis" @default.
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- W2984057763 doi "https://doi.org/10.1016/j.im.2019.103219" @default.
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