Conversational Analysis of Daily Dialog Data using Polite Emotional Dialogue Acts
This work provides insights into socio-linguistic patterns for researchers in conversational AI and linguistics, but it is incremental as it confirms expected correlations without introducing new methods.
The study analyzed daily dialog data to examine associations between politeness, emotion, and dialogue acts, finding that Anger and Disgust correlate with impoliteness while Happiness and Sadness correlate with politeness, and Inform and Commissive acts are more polite than Question and Directive acts.
Many socio-linguistic cues are used in conversational analysis, such as emotion, sentiment, and dialogue acts. One of the fundamental cues is politeness, which linguistically possesses properties such as social manners useful in conversational analysis. This article presents findings of polite emotional dialogue act associations, where we can correlate the relationships between the socio-linguistic cues. We confirm our hypothesis that the utterances with the emotion classes Anger and Disgust are more likely to be impolite. At the same time, Happiness and Sadness are more likely to be polite. A less expectable phenomenon occurs with dialogue acts Inform and Commissive which contain more polite utterances than Question and Directive. Finally, we conclude on the future work of these findings to extend the learning of social behaviours using politeness.