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Type :thesis
Subject :TK Electrical engineering. Electronics Nuclear engineering
Main Author :Noor Alhusna Madzlan
Title :Development of an automatic attitude recognition system: a multimodal analysis of video blogs
Place of Production :Tanjong Malim
Publisher :Fakulti Bahasa dan Komunikasi
Year of Publication :2017
Corporate Name :Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris
Communicative  content  in  human  communication  involves  expressivity  of  socio-affective  states.  Research  in  Linguistics,  Social Signal  Processing  and  Affective Computing  in  par­  ticular, 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 expres­ sion 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 biogs.  The present  study  de­  scribes a new communicative genre of self-expression through social media:  video blogging, which  provides opportunities  for interlocutors  to disseminate information  through  a myriad of multi  modal characteristics.  This study describes main features of this novel communica­ tion medium and  focuses attention to its possible exploitation  as a rich source of information for human  communication. The dissertation describes manual annotation of attitude expres­ sions 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  biogs  is  elaborated  and  five  attitude  labels  are  derived.   Prosodic  and  visual  fea­ ture  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  

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