PDF-(EBOOK)-Affective Computing and Sentiment Analysis Emotion Metaphor and Terminology (Text

This volume maps the watershed areas between two holy grails of computer science the identification and interpretation of affect 8211 including sentiment and mood The expression of sentiment and mood involves the use of metaphors especially in emotive situations Affect computing is rooted in hermeneutics philosophy political science and sociology and is now a key area of research in computer science The 247 news sites and blogs facilitate the expression and shaping of opinion locally and globally nbspSentiment analysis based on text and data mining is being used in the looking at news and blogs for purposes as diverse as brand management film reviews financial market analysis and prediction homeland security There are systems that learn how sentiments are articulated This work draws on and informs research in fields as varied as artificial intelligence especially reasoning and machine learning corpusbased information extraction linguistics and psychologynbsp. Affective, Sentimental, and Connotative Meaning in the Lexicon. Affective meaning. Drawing on literatures in. affective computing (Picard 95). l. inguistic subjectivity (. Wiebe. and colleagues). s. : Interactive Visualization for Exploring and Comparing the Sentiments of Time-Varying Twitter Data. Florence Ying Wang, Arnaud . Sallaberry. , Mathieu Roche. LIRMM & . Universite. Montpellier, . Professor Beste . Filiz. . Yuksel. University of San Francisco. CS 686/486. Inspired by Prof. Rosalind Picard’s Affective Computing class . https://ocw.mit.edu/courses/media-arts-and-sciences/mas-630-affective-computing-fall-2015/. David Gallagher. What is Emotional Annotation of Text?. Emotion complexity. Emotional connotation. Approaches. Emotional Categories. “Bag of Words”. Emotional Dimensions. Plutchik’s. Wheel. Why research Emotional Annotation of Text?. CSE, HKUST. March . 27. Recap. What have we done for the project? . Phase . I: Data . Collection. Collect . data from the . Web. . (at least 4 data sources). Parse the data using different tool. Design . C. omputing in Computer Games. Dr. . Saeid. . Pourroostaei. . Ardakani. Faculty of Psychology and Educational Science. Allameh. . Tabataba’i. University. Autumn 2016. Outline. Introduction to Affective Computing. from Affect Sciences to . Computational . Models. Dr.. Alexandra . Balahur. European Commission Joint Research Centre. alexandra.balahur@jrc.ec.europa.eu. . 22 April 2016. Tutorial held at the . 10th edition of the Language Resources and Evaluation Conference, 23-28 May 2016, Portorož (Slovenia). Affective, Sentimental, and Connotative Meaning in the Lexicon. Affective meaning. Drawing on literatures in. affective computing (Picard 95). l. inguistic subjectivity (. Wiebe. and colleagues). s. Talks held at 4201 Wilson Blvd, Arlington, VA 22230 Questions: Contact Jasmine Young at jyoung@nsf.gov. CISE DISTINGUISHED LECTURE SERIES . Inventing the Future of Technology with Emotion. Positive or negative movie review?. unbelievably . disappointing . Full of . zany characters and richly applied satire, and some great plot . twists. this is the greatest screwball comedy ever . filmed. & Figurative Devices. Figurative language . is when a writer uses . figures of speech to be more effective, . persuasive, or . impactful. . Figures . of speech such as metaphors, similes. , . or allusions . Positive or negative movie review?. unbelievably . disappointing . Full of . zany characters and richly applied satire, and some great plot . twists. this is the greatest screwball comedy ever . filmed. This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications 8211 gradually more advance information is provided giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA) Regularized Discriminant Analysis (RDA) Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features and feature fusion techniques. This volume covers key topics in the field from a variety of leading researchers. In one volume readers gain exposure to several perspectives in the areas of corpus annotation and analysis dialogue system construction theoretical perspectives on communicative intention context-based generation and modeling of discourse structure. Based on the 2nd SIGdial workshop on Discourse and Dialogue held in conjunction with Eurospeech 2001 it is of interest to researchers and practitioners in dialogue and discourse processing.