Subject Index
Note: Page numbers followed by f indicate figures and t indicate tables.
A
Abstractive summarization
crude features and user-defined feature
175
product reviews, discourse structure of
176
Affective commonsense reasoning
71–72
Affective Norms for English Words (ANEW)
36,
61
Appraisal theories, emotions
53
ArsEmotica Ontology of Emotions
38
Aspect and sentiment unification model (ASUM)
99
Aspect-based sentiment analysis
31,
59–60
B
Bayes classification algorithm
194–195
Blog content, author, reader properties, relationship (BARR)
164
C
Collective positive-unlabeled (CPU) learning
141
Compositional sentiment parsing
228–229
Concept-level sentiment analysis
31,
42–43,
73
Content-based social networks
17
Contextual valence shifters
41–42
material announcements
224
real-time opinion streaming
comparative expressions
232
compositional sentiment parsing
228–229
core natural language processing
226–227
relational sentiment analysis
230
sentiment confidence estimation
229–230
D
Dianping’s filtering system
150–151
Dictionary of Affect in Language (DAL)
36,
116
Dimensional models, emotions
52–53
Distributional semantics
41
Domain-dependent lexica
33
Domain-specific sentiment lexicon
64–65,
64f
E
Eigenvector centrality
159
Emotion Markup Language (EmotionML)
54–56
corpus of Japanese tweets
199
emotion lexicon, lemon
62
SemEval campaigns, tasks on
199
Emotion Tweet Corpus for Classification (ETCC)
199–200,
200t
European Semantic Web Conference
31
Expertise, novelty, influence, and activity (ENIA)
165
crude features and user-defined feature
175
Extrinsic summary evaluation
173–174
F
sarcastic and ironic sentences
8–9
FinancialTwitterTracker
65–66
Friedkin-Johnsen influence model
163
G
Grice’s theory of irony
113
H
Hourglass of emotions model
Minsky’s theory of mind
75
three-dimensional model of
75,
76f
Hybrid Operable Platform for Language Management and Extensible Semantics (HOLMES)
201,
202f
I
InfluenceRank algorithm
164
Interoperability, language resources
49
Intrinsic summary evaluation
definition and theories
113
classification approach
116
real-time opinion streaming
226
positive and negative terms, frequency of
114
Iterative classification algorithm (ICA)
152–153
J
Joint sentiment topic (JST) model
99
K
life insurance organization
220–221
social media analytics
220f
codebook/coding framework
219
statistical predictive model
219
social media and sentiment philosophy
214–215
L
Lexicon Model for Ontologies (lemon)
domain-specific sentiment lexicon
64–65,
64f
Linguistic Inquiry and Word Count (LIWC)
36
Linguistic linked open data
Marl ontology, sentiment analysis
domain-specific sentiment lexicon
64–65,
64f
sentiment corpus, NIF
57–60
sentiment lexicons, lemon
61–62
sentiment services, NIF
62,
63t
emotion lexicon, lemon
62
Linguistic linked open data (LLOD) cloud
49
Linked open data (LOD) project
49
Longitudinal user centered influence model
163
Loopy belief propagation (LBP)
142,
146
M
Machine learning, online social networks
relationships and natural language
101f
semisupervised learning
102,
105
Macquarie Semantic Orientation Lexicon
115–116
Markov random fields (MRFs)
142–146
MaxDiff Twitter Sentiment Lexicon
34
Microblogging social networks
17
Minsky’s theory of mind
75
N
NRC Hashtag Affirmative Context Sentiment Lexicon
34
NRC Hashtag Negated Context Sentiment Lexicon
34
O
Online social networks
13–14
indexes and metrics
17–18
explicit and implicit information
92
intelligence applications
103
medical field, application in
103
psychological and motivational factors
self-presentation and impression management
19
social networks analytics
social network analysis
21–22
user-generated content, types of
16–17
Ontology for Media Resources (OMR)
79–80,
80f
Open Linguistics Working Group
49
interaction information and content
165–166
personification of certain values
157–158
two-step flow of communication model
157–158
Opinion Seer visualization system
180,
184
Opinion summarization
241
abstractive summarization
news articles and scientific journals
171–172
Opinion visualization
171,
241
explore opinions on single entity
179–180
feature-based sentiment analysis
178
interactive techniques
177
large-scale events, user reactions to
178,
178t,
181
P
Plutchik’s circumplex model
37–38
Polarity classification
3–4
explicit and implicit information
92
relationships and natural language
101f
semisupervised learning
102,
105
Positive opinion leader detection method
164
Positive-unlabeled (PU) learning
150
Profile-based social networks
16
PROV Ontology (PROV-O)
50
Q
R
Relational sentiment analysis
230
Resource Description Framework (RDF)
37,
49
loopy belief propagation
146
S
behavioral modeling approach
118
binary classification experiments
118
bootstrapping algorithm
118
real-time opinion streaming
226
semisupervised algorithm
117
concept-level resources and ontology
37–38
concept-level sentiment analysis
31,
42–43
distributional semantics
41
entities, properties, and relations
41–42
fine emotion categories, annotated corpora for
38–40
lexical information
40–41
psycholinguistic resources
36
sentiment resources
32–34
Semisupervised machine learning approach
lexical-based approaches
98
relationships and natural language
Semisupervised subjective feature weighting and intelligent model selection (SWIMS)
96–97
affective commonsense reasoning
71–72
hourglass of emotions model
75–77
social media marketing
79–82
Twitter sentiment analysis system
82–85,
83f
Sentiment140 Affirmative Context Lexicon
34
Sentiment analysis (SA)
198
knowledge-based systems
73
vs. opinion, definitions
1–2
statistical-based approaches
73–74
subjective and objective sentences
5–6,
6f
Sentiment140 NRC Twitter Sentiment Lexicon
61
Senti-Miner, emotion analysis
dependency graph transformation
204,
205f
token-based regular expression annotation
203,
204f
Smart Media Management for Social Customer Attention
See SOMA
Smart social customer relationship management
See SOMA
data-driven marketing
212
Social network analysis (SNA)
explicit
vs. implicit opinions
7–8
regular
vs. comparative opinion
emotional tweets, detection of
vs. nonemotional tweets, symbolic methods for
207,
207f,
208t
Source credibility theory
20–21
Mozilla Public License version 2.0
189
open source business intelligence suite
189,
195
Social Network Analysis module
marketing campaign monitoring and analysis
192–193
naïve Bayes classification algorithm
194–195
language inconsistency
154
multiple site comparisons
154
loopy belief propagation
146
Twitter, campaign promoters
opinion spam/spammer evaluation
149–150
SPARQL queries, sentiment lexicons
65
Status homophily relationships
93
semisupervised learning
102
Subjectivity Lexicon
32–33
Subjectivity Sense Annotations
33
political discussion forums
130,
133
rule-based classifiers
134
statement classification task
131–132
statistical classifiers
134
travel discussion forums
130,
133
wish detection problem
129
Sum-product algorithm
146
Supervised machine learning approach
graphical representation
96f
naïve Bayes and neural network classifiers
95–96
rule-based classifier
96–97
support vector machines
96–97
relationships and natural language
Supervised text classification
150
T
Tag cloud–based visualization
180
Text Encoding Initiative
49
Topic sentiment mixture model
99
Jakarta and Mumbai terrorist attacks (2009), user reactions to
10,
103
KRC Research, engagement rate
216,
216f
sentiment classification, sentic computing
82–85,
83f
SpagoBI Social Network Analysis module
190–192
opinion spam/spammer evaluation
149–150
Typed Markov random fields (T-MRF)
147–149
U
Uniform resource identifiers (URIs)
56–58
Unsupervised machine learning approach
relationships and natural language
V
Value homophily relationships
93
semisupervised learning
102
unsupervised learning
103
W
World Wide Web Consortium (W3C)
49
Y