TY - BOOK ID - 33241096 TI - Multimodal Sentiment Analysis AU - Poria, Soujanya. AU - Hussain, Amir. AU - Cambria, Erik. PY - 2018 SN - 3319950207 3319950185 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computational intelligence. KW - Neurosciences. KW - Multimedia systems. KW - Computer vision. KW - Natural language processing (Computer science). KW - Translating and interpreting. KW - Multimedia Information Systems. KW - Image Processing and Computer Vision. KW - Natural Language Processing (NLP). KW - Translation. KW - Interpretation and translation KW - Interpreting and translating KW - Language and languages KW - Literature KW - Translation and interpretation KW - Translators KW - NLP (Computer science) KW - Artificial intelligence KW - Electronic data processing KW - Human-computer interaction KW - Semantic computing KW - Machine vision KW - Vision, Computer KW - Image processing KW - Pattern recognition systems KW - Computer-based multimedia information systems KW - Multimedia computing KW - Multimedia information systems KW - Multimedia knowledge systems KW - Information storage and retrieval systems KW - Neural sciences KW - Neurological sciences KW - Neuroscience KW - Medical sciences KW - Nervous system KW - Translating KW - Multimedia information systems. KW - Optical data processing. KW - Translation and interpretation. KW - Optical computing KW - Visual data processing KW - Bionics KW - Integrated optics KW - Photonics KW - Computers KW - Optical equipment KW - Natural language processing (Computer science) UR - https://www.unicat.be/uniCat?func=search&query=sysid:33241096 AB - This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis. ER -