ORI GIN AL PA PER Spontaneous speech and opinion detection: mining call-centre transcripts

Abstract Opinion mining on conversational telephone speech tackles two chal-lenges: 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 informa-tion extraction module is evaluated by comparing its outputs on manual versus automatic transcripts. The F-scores obtained are 0.79 for business concepts detec-tion, 0.74 for opinion detection and 0.67 for the extraction of relations betwee