Language Resources and Evaluation

, Volume 47, Issue 4, pp 1089–1125 | Cite as

Spontaneous speech and opinion detection: mining call-centre transcripts

  • Chloé ClavelEmail author
  • Gilles Adda
  • Frederik Cailliau
  • Martine Garnier-Rizet
  • Ariane Cavet
  • Géraldine Chapuis
  • Sandrine Courcinous
  • Charlotte Danesi
  • Anne-Laure Daquo
  • Myrtille Deldossi
  • Sylvie Guillemin-Lanne
  • Marjorie Seizou
  • Philippe Suignard
Original Paper


Opinion mining on conversational telephone speech tackles two challenges: the robustness of speech transcriptions and the relevance of opinion models. The two challenges are critical in an industrial context such as marketing. The paper addresses jointly these two issues by analyzing the influence of speech transcription errors on the detection of opinions and business concepts. We present both modules: the speech transcription system, which consists in a successful adaptation of a conversational speech transcription system to call-centre data and the information extraction module, which is based on a semantic modeling of business concepts, opinions and sentiments with complex linguistic rules. Three models of opinions are implemented based on the discourse theory, the appraisal theory and the marketers’ expertise, respectively. The influence of speech recognition errors on the information extraction module is evaluated by comparing its outputs on manual versus automatic transcripts. The F-scores obtained are 0.79 for business concepts detection, 0.74 for opinion detection and 0.67 for the extraction of relations between opinions and their target. This result and the in-depth analysis of the errors show the feasibility of opinion detection based on complex rules on call-centre transcripts.


Call-centre data Automatic speech recognition system Opinion detection Business concept detection Disfluency 



This work was partly financed by CAP DIGITAL, the Business Cluster for digital content through the VoxFactory project.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Chloé Clavel
    • 1
    Email author
  • Gilles Adda
    • 2
  • Frederik Cailliau
    • 3
  • Martine Garnier-Rizet
    • 4
  • Ariane Cavet
    • 3
  • Géraldine Chapuis
    • 4
  • Sandrine Courcinous
    • 5
  • Charlotte Danesi
    • 1
  • Anne-Laure Daquo
    • 4
  • Myrtille Deldossi
    • 6
  • Sylvie Guillemin-Lanne
    • 6
  • Marjorie Seizou
    • 6
  • Philippe Suignard
    • 1
  1. 1.EDF R&DClamartFrance
  2. 2.LIMSIUniversité Paris XIOrsay CedexFrance
  3. 3.SinequaParisFrance
  4. 4.VecsysLes UlisFrance
  5. 5.Vocapia Research, 3 rue Jean RostandParc Orsay UniversitéOrsayFrance
  6. 6.TEMISParisFrance

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