Spontaneous speech and opinion detection: mining call-centre transcripts

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 o...

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Bibliographic Details
Published in:	Language Resources and Evaluation Vol. 47; no. 4; pp. 1089 - 1125
Main Authors:	Clavel, Chloé, Adda, Gilles, Cailliau, Frederik, Garnier-Rizet, Martine, Cavet, Ariane, Chapuis, Géraldine, Courcinous, Sandrine, Danesi, Charlotte, Daquo, Anne-Laure, Deldossi, Myrtille, Guillemin-Lanne, Sylvie, Seizou, Marjorie, Suignard, Philippe
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer 01-12-2013 Springer Netherlands Springer Nature B.V Springer Verlag
Subjects:	Applied linguistics Appraisal theory Attitudes Business Call centers Computational Linguistics Computer Science Conversation Data mining Errors Extraction Feasibility studies Information retrieval Keywords Language and Literature Linguistics Modules Original Paper Semantic analysis Semantics Sentiment analysis Social Sciences Speech recognition Spontaneous speech Telephones Transcription Voice recognition Words France Opinion detection Disfluency Automatic speech recognition system Business concept detection Call-centre data Transcription Telephone Semantic analysis Conversation Annotation Automatic speech recognition French Natural language processing Opinion mining
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Summary:	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.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	1574-020X 1572-8412 1574-0218
DOI:	10.1007/s10579-013-9224-5