Deception Detection and Analysis in Spoken Dialogues based on FastText
| Autor: | Koichiro Yoshino, Satoshi Nakamura, Sakriani Sakti, Naoki Hosomi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Speech recognition media_common.quotation_subject 05 social sciences Supervised learning Sentiment analysis Detector Feature extraction 050109 social psychology Deception 0502 economics and business Task analysis Detecting deception 0501 psychology and cognitive sciences Classifier (UML) 050203 business & management media_common |
| Zdroj: | APSIPA |
| DOI: | 10.23919/apsipa.2018.8659614 |
| Popis: | Detecting deception is complicated for humans even though it often happens in human communications. In contrast, machines can capture small features to achieve accurate deception-detection, which is difficult for humans. Classifiers based on supervised learning make it possible to analyze effective features for deception-detection by giving positive and negative samples of deception to the classifier. FastText is one accurate classifier for a variety of classification problems, sentiment analysis, or the tagging of sentences, all of which use the distributed representation of features. We constructed a deception detector for dialogue utterances by giving labels of deception to FastText. We also combined acoustic features for deception-detection and analyzed the deception-detection results. The resultant detector achieved significantly higher accuracy than deception-detection by humans. |
| Databáze: | OpenAIRE |
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