Affective Computing and Sentiment Analysis: Emotion, Metaphor and Terminology

Introduction: Affect Computing and Sentiment Analysis . References .- 1. Understanding Metaphors: The Paradox of Unlike Things Compared by Sam Glucksberg . 1.1 Introduction . 1.2 The Metaphor Paraphrased Problem and the Priority of the Literal . 1.3 Understanding Metaphors: Comparison or Categorization? . 1.4 How Novel Categories Can be Named: Dual Reference . 1.5 Understanding Metaphors and Similes . 1.6 The Metaphor Paraphrase Problem Revisited . 1.7 Comparison Versus Categorization Revisited . 1.8 Conclusions . References .- 2. Metaphor as Resources for the Conceptualisation and Expression of Emotion by Andrew Goatly . 2.1 Background . 2.2 Metaphorical Conceptualisation of Emotions in English . 2.2.1 Conceptualisation of Emotion . 2.2.2 Description and Expression of Emotion . 2.3 Contribution of English Metaphor Themes to the Expression of Emotion . 2.3.2 Metalude Data for Evaluation . 2.3.2 Evaluative Transfer . 2.3.3 Evaluation Dependent on Larger Schemata . 2.3.4 Ideology and Evaluation . 2.3.5 The Role of Multivalency and Opposition in Metaphor Themes . 2.4 Conclusion . References .- 3. The Deep Lexical Semantics of Emotions by Jerry R. Hobbs and Andrew Gordon . 3.1 Introduction . 3.2 Identifying the Core Emotion Words . 3.3 Filling out the Lexicon of Emotion . 3.4 Some Core Theories . 3.5 The Theory and Lexical Semantics of Emotion . 3.6 Summary . References .- 4. Genericity and Metaphoricity Both Involve Sense Modulation by Carl Vogel . 4.1 Background . 4.2 Dynamics of First-order Information . 4.2.1 Some Intuitions About Revision . 4.2.2 A Formal Model of First-order Belief Revision . 4.2.3 First-order Belief Revision Adapted to Sense Extension . 4.3 Ramifications for Metaphoricity . 4.4 Metaphoricity and Genericity . 4.5 Particulars of the Class-Inclusion Framework . 4.6 Final Remarks . References .- 5. Affect Transfer by Metaphor for an Intelligent Conversational Agent by Alan Wallington, Rodrigo Agerri, John Barnden, Mark Lee and Tim Rumbell . 5.1 Introduction . 5.2 Affect via Metaphor in an ICA . 5.3 Metaphor Processing . 5.3.1 The Recognition Component . 5.3.2 The Analysis Component . 5.4 Examples of the Course of Processing . 5.4.1 You Piglet . 5.4.2 Lisa is an Angel . 5.4.3 Mayid is a Rock . 5.4.4 Other Examples . 5.5 Results . 5.6 Conclusions and Further Work . References .- 6. Detecting Uncertainty in Spoken Dialogues: An Explorative Research for the Automatic Detection of Speaker Uncertainty by Using Prosodic Markers by Jeroen Dral, Dirk Heylen and Rieks op den Akker . 6.1 Introduction . 6.2 Related Work 6.2.1 Defining (un)certainty . 6.2.2 Linguistic Pointers to Uncertainty . 6.2.3 Prosodic Markers of Uncertainty . 6.3 Problem Statement . 6. 4 Data Selection . 6.4.1 Selection of Meetings . 6.4.2 Data Preparation and Selection . 6.4.3 Statistical Analysis . 6.5 Experimentation . 6.5.1 Hedges –vs-No Hedges . 6.5.2 Uncertain Hedges-vs-Certain Hedges . 6.5.3 Distribution of Hedges over Dialog Acts . 6.6 Conclusions . References .- 7. Metaphors and Metaphor-like Processes Across Languages: Notes on English and Italian Language of Economics by Maria Teresa Musacchio . 7.1 Introduction . 7.2 Corpus and Method . 7.2.1 Corpus . 7.2.2 Method . 7.3 Analysis . 7.3.1 Constitutive Metaphors . 7.3.2 Pedagogic Metaphors . 7.3.3 Universal vs Culture-specific Metaphors . 7.4 Conclusion . References .- 8. The ‘Return’ and ‘Volatility’ of Sentiments: An Attempt to Quantify the Behaviour of the Markets? by Khurshid Ahmad . 8.1 Introduction . 8.2 Metaphors of ‘Return’ and ‘Volatility’ . 8.3 The Roots of Computational Sentiment Analysis . 8.4 A Corpus-based Study of Sentiments, Terminology and Ontology over Time . 8.4.1 Corpus Preparation and Composition . 8.4.2 Candidate Terminology and Ontology . 8.4.3 Historical Volatility in our Corpus . 8.5 Afterword . References .- 9 Sentiment Analysis Using Automatically Labelled Financial News Items by Michel Généreux, Thierry Poibeau and Moshe Koppel . 9.1 Introduction . 9.2 Data and Method . 9.2.1 Training and Testing Corpus . 9.2.2 Feature Types . 9.2.3 Feature Selection and Counting Methods . 9.2.4 News Items and Stock Prices Correlation . 9.2.5 Feature Selection and Semantic Relatedness of Documents . 9.3 Results . 9.3.1 Horizon Effect . 9.3.2 Polarity Effect . 9.3.3 Range Effect . 9.3.4 Effect of Adding a Neutral Class on Non-Contemporaneous Prices: One- and Two Days Ahead . 9.3.5 Conflating Two Classes . 9.3.6 Positive and Negative Features . 9.4 Discussion . 9.5 Conclusion and Future Work . References .- 10 Co-Word Analysis for Assessing Consumer Associations: A Case Study in Market Research by Thorsten Teichert, Gerhard Heyer, Katja Schöntag and Patrick Mairif . 10.1 Introduction . 10.2 Conceptual Background . 10.2. 1 Consumer Associations and Mental Processing . 10.2.2 Drawbacks of Manual Data Analysis . 10.2.3 Requirements for Automated Co-Word Analysis . 10.3 Technique and Implementation . 10 3. 1 Import of Text Sources . 10.3.2 Processing of Text . 10.3.3 Graph Creation and Clustering . 10.4 Exemplary Case Study . 10.5 Conclusion and Outlook . References .- 11 Automating Opinion Analysis in Film Reviews: The Case of Statistic Versus Linguistic Approach by Damien Poirier, Cécile Bothorel, Émilie Guimier De Neef, and Marc Boullé . 11.1 Introduction . 11.2 Related Work . 11.2.1 Machine Learning for Opinion Analysis . 11.2.2 Linguistic Methods of Opinion Analysis . 11.2.2 Linguistic Methods of Opinion Analysis . 11.3 Linguistic and Machine Learning Methods: A Comparative Study . 11.3.1 Linguistic Approach . 11.3.2 Machine Learning Approach . 11.4 Conclusion and Prospects . References .- Afterword: ‘The Fire Sermon’ by Yorick Wilks . References .- Name Index .- Subject Index