A text analytics framework for automated communication pattern analysis

2020 | Information & Management | Citations: 4

Authors: Fan, Shaokun; Ilk, Noyan

Abstract: Human-coding reliant conversation analysis methods are ineffective when analyzin ... Expand

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. Collapse

Topics: customer satisfaction customer service process mining knowledge sharing service quality

Methods: computational algorithm case study qualitative content analysis ensemble learning machine learning

Theories: speech act theory linguistics theory