Development of an automatic attitude recognition system: a multimodal analysis of video blogs
Citation:MADZLAN, NOOR ALHUSNA, Development of an automatic attitude recognition system: a multimodal analysis of video blogs, Trinity College Dublin.School of Linguistic Speech & Comm Sci, 2017
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Communicative content in human communication involves expressivity of socio-affective states. Research in Linguistics, Social Signal Processing and Affective Computing in particular, highlights the importance of affect, emotion and attitudes as sources of information for communicative content. Attitudes, considered as socio-affective states of speakers, are conveyed through a multitude of signals during communication. Understanding the expression of attitudes of speakers is essential for establishing successful communication. Taking the empirical approach to studying attitude expressions, the main objective of this research is to contribute to the development of an automatic attitude classification system through a fusion of multimodal signals expressed by speakers in video blogs. The present study describes a new communicative genre of self-expression through social media: video blogging, which provides opportunities for interlocutors to disseminate information through a myriad of multimodal characteristics. This study describes main features of this novel communication medium and focuses attention to its possible exploitation as a rich source of information for human communication. The dissertation describes manual annotation of attitude expressions from the vlog corpus, multimodal feature analysis and processes for development of an automatic attitude annotation system. An ontology of attitude annotation scheme for speech in video blogs is elaborated and five attitude labels are derived. Prosodic and visual feature extraction procedures are explained in detail. Discussion on processes of developing an automatic attitude classification model includes analysis of automatic prediction of attitude labels using prosodic and visual features through machine-learning methods. This study also elaborates detailed analysis of individual feature contributions and their predictive power to the classification task.
Ministry of Higher Education Malaysia
Author: MADZLAN, NOOR ALHUSNA
Publisher:Trinity College Dublin. School of Linguistic Speech & Comm Sci. C.L.C.S.
Type of material:Thesis
Availability:Full text available