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Identifying Conversational Message Threads by Integrating Classification and Data Clustering

  • Giacomo DomeniconiEmail author
  • Konstantinos Semertzidis
  • Gianluca Moro
  • Vanessa Lopez
  • Spyros Kotoulas
  • Elizabeth M. Daly
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 737)

Abstract

Conversational message thread identification regards a wide spectrum of applications, ranging from social network marketing to virus propagation, digital forensics, etc. Many different approaches have been proposed in literature for the identification of conversational threads focusing on features that are strongly dependent on the dataset. In this paper, we introduce a novel method to identify threads from any type of conversational texts overcoming the limitation of previously determining specific features for each dataset. Given a pool of messages, our method extracts and maps in a three dimensional representation the semantic content, the social interactions and the timestamp; then it clusters each message into conversational threads. We extend our previous work by introducing a deep learning approach and by performing new extensive experiments and comparisons with classical learning algorithms.

Keywords

Conversational message Thread identification Data clustering Classification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Giacomo Domeniconi
    • 1
    Email author
  • Konstantinos Semertzidis
    • 2
  • Gianluca Moro
    • 1
  • Vanessa Lopez
    • 3
  • Spyros Kotoulas
    • 3
  • Elizabeth M. Daly
    • 3
  1. 1.Department of Computer Science and Engineering (DISI)University of Bologna at CesenaCesenaItaly
  2. 2.Department of Computer Science and EngineeringUniversity of IoanninaIoanninaGreece
  3. 3.IBM Research - Damastown Industrial Estate MulhuddartDublin 15Ireland

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