Acknowledgments	6
	Introduction: Affect Computing and SentimentAnalysis	7
	References	10
	Contents	11
	Contributors	13
	1 Understanding Metaphors: The Paradox of Unlike Things Compared	15
	1.1 Introduction	15
	1.2 The Metaphor Paraphrase Problem and the Priority of the Literal	16
	1.3 Understanding Metaphors: Comparison or Categorization?	17
	1.4 How Novel Categories Can Be Named: Dual Reference	18
	1.5 Understanding Metaphors and Similes	20
	1.6 The Metaphor Paraphrase Problem Revisited	22
	1.7 Comparison Versus Categorization Revisited	23
	1.8 Conclusions	24
	References	25
	2 Metaphor as Resource for the Conceptualisation and Expression of Emotion	27
	2.1 Background	27
	2.2 Metaphorical Conceptualisation of Emotions in English	28
	2.2.1 Conceptualisation of Emotion	28
	2.2.2 Description and Expression of Emotion	30
	2.3 Contribution of English Metaphor Themes to the Expression of Emotion	31
	2.3.1 Metalude Data for Evaluation	31
	2.3.2 Evaluative Transfer	32
	2.3.3 Evaluation Dependent on Larger Schemata	34
	2.3.4 Ideology and Evaluation	35
	2.3.5 The Role of Multivalency and Opposition in Metaphor Themes	37
	2.4 Conclusion	39
	References	39
	3 The Deep Lexical Semantics of Emotions	40
	3.1 Introduction	40
	3.2 Identifying the Core Emotion Words	41
	3.3 Filling Out the Lexicon of Emotion	41
	3.4 Some Core Theories	43
	3.5 The Theory and Lexical Semantics of Emotion	44
	3.6 Summary	46
	References	47
	4 Genericity and Metaphoricity Both Involve Sense Modulation	48
	4.1 Background	48
	4.2 Dynamics of First-Order Information	51
	4.2.1 Some Intuitions About Revision	51
	4.2.2 A Formal Model of First-Order Belief Revision	52
	4.2.3 First-Order Belief Revision Adapted to Sense Extension	53
	4.3 Ramifications for Metaphoricity	55
	4.4 Metaphoricity and Genericity	57
	4.5 Particulars of the Class-Inclusion Framework	60
	4.6 Final Remarks	63
	References	63
	5 Affect Transfer by Metaphor for an Intelligent Conversational Agent	65
	5.1 Introduction	65
	5.2 Affect via Metaphor in an ICA	67
	5.3 Metaphor Processing	68
	5.3.1 The Recognition Component	68
	5.3.2 The Analysis Component	70
	5.4 Examples of the Course of Processing	73
	5.4.1 You Piglet	73
	5.4.2 Lisa Is an Angel	74
	5.4.3 Mayid Is a Rock	74
	5.4.4 Other Examples	74
	5.5 Results	75
	5.6 Conclusions and Further Work	76
	References	77
	6 Detecting Uncertainty in Spoken Dialogues: An Exploratory Research for the Automatic Detection of Speaker Uncertainty by Using Prosodic Markers	79
	6.1 Introduction	79
	6.2 Related Work	79
	6.2.1 Defining (Un)certainty	79
	6.2.2 Linguistic Pointers to Uncertainty	81
	6.2.3 Prosodic Markers of Uncertainty	81
	6.3 Problem Statement	82
	6.4 Data Selection	83
	6.4.1 Selection of Meetings	83
	6.4.2 Data Preparation and Selection	83
	6.4.3 Statistical Analysis	84
	6.5 Experimentation	85
	6.5.1 Hedges --vs-- No Hedges	85
	6.5.2 Uncertain Hedges --vs-- Certain Hedges	86
	6.5.3 Distribution of Hedges Over Dialogue Acts	88
	6.6 Conclusions	88
	References	89
	7 Metaphors and Metaphor-Like Processes Across Languages: Notes on English and Italian Language of Economics	90
	7.1 Introduction	90
	7.2 Corpus and Method	92
	7.2.1 Corpus	92
	7.2.2 Method	92
	7.3 Analysis	93
	7.3.1 Constitutive Metaphors	93
	7.3.2 Pedagogic Metaphors	96
	7.3.3 Universal vs. Culture-Specific Metaphors	96
	7.4 Conclusion	97
	References	98
	8 The `Return' and `Volatility' of Sentiments: An Attempt to Quantify the Behaviour of the Markets?	100
	8.1 Introduction	100
	8.2 Metaphors of `Return' and of `Volatility'	100
	8.3 The Roots of Computational Sentiment Analysis	103
	8.4 A Corpus-Based Study of Sentiments, Terminology and Ontology Over Time	104
	8.4.1 Corpus Preparation and Composition	105
	8.4.2 Candidate Terminology and Ontology	105
	8.4.3 Historical Volatility in Our Corpus	106
	8.5 Afterword	108
	References	109
	9 Sentiment Analysis Using Automatically Labelled Financial News Items	111
	9.1 Introduction	111
	9.2 Data and Method	112
	9.2.1 Training and Testing Corpus	112
	9.2.2 Feature Types	112
	9.2.3 Feature Selection and Counting Methods	113
	9.2.4 News Items and Stock Price Correlation	114
	9.2.5 Feature Selection and Semantic Relatedness of Documents	115
	9.3 Results	116
	9.3.1 Horizon Effect	116
	9.3.2 Polarity Effect	117
	9.3.3 Range Effect	118
	9.3.4 Effect of Adding a Neutral Class on Non-cotemporaneous Prices: One- and Two-Days Ahead	118
	9.3.5 Conflating Two Classes	119
	9.3.6 Positive and Negative Features	120
	9.4 Discussion	121
	9.4.1 Lack of Independent Testing Corpus	121
	9.4.2 Pool of Features	122
	9.4.3 Size of Documents	122
	9.4.4 Trading Costs	122
	9.5 Conclusion and Future Work	122
	References	123
	10 Co-Word Analysis for Assessing Consumer Associations: A Case Study in Market Research	125
	10.1 Introduction	125
	10.2 Conceptual Background	126
	10.2.1 Consumer Associations and Mental Processing	126
	10.2.2 Drawbacks of Manual Data Analysis	127
	10.2.3 Requirements for Automated Co-Word Analysis	127
	10.3 Technique and Implementation	128
	10.3.1 Import of Text Sources	129
	10.3.2 Processing of Text	129
	10.3.3 Graph Creation and Clustering	129
	10.4 Exemplary Case Study	130
	10.5 Conclusion and Outlook	132
	References	133
	11 Automating Opinion Analysis in Film Reviews: The Case of Statistic Versus Linguistic Approach	135
	11.1 Introduction	135
	11.2 Related Work	136
	11.2.1 Machine Learning for Opinion Analysis	136
	11.2.2 Linguistic Methods of Opinion Analysis	137
	11.3 Linguistic and Machine Learning Methods: A Comparative Study	140
	11.3.1 Linguistic Approach	140
	11.3.2 Machine Learning Approach	143
	11.4 Conclusion and Prospects	147
	References	148
	Afterword: `The Fire Sermon'	151
	Name Index	155
	Subject Index	157