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