Subject Index
Note: Page numbers followed by f indicate figures and t indicate tables.
A
crude features and user-defined feature 175
definition 172
noisy documents 172–173
opinion-filled text 173
product reviews, discourse structure of 176
steps 172
Advertisements 226
Advice mining 129–130
Affective commonsense reasoning 71–72
Affective Lexicon 73
Affective Reasoner 73
AFINN lexicon 34
Amazon 136–137
Amazon Lexicon 33
Amplification metrics 17
Applause metrics 17
Appraisal theories, emotions 53
ArsEmotica Ontology of Emotions 38
Aspect and sentiment unification model (ASUM) 99
B
Bag-of-concepts model 85
Bag-of-words model 85
Bayes classification algorithm 194–195
Bayesian inference 73–74
Betweenness centrality 159–160
Blog content, author, reader properties, relationship (BARR) 164
C
Categorical models 53
Closeness centrality 159–160
Cockpits 189–190
Collective positive-unlabeled (CPU) learning 141
collective classification 152–153
evaluation 153–154
problem definition 150–152
Compositional sentiment parsing 228–229
Content-based social networks 17
Content mining 164–165
Contextual valence shifters 41–42
Conversation rate 18
ConVis 181–184
financial announcements 223–224
material announcements 224
public conference 223–224
affect analysis 230–231
comparative expressions 232
complications 234
compositional sentiment parsing 228–229
core natural language processing 226–227
data sources and filters 225–226
entity scoring 229
irrealis mood 231–232
opinion metrics 227
practical use cases 227–228
relational sentiment analysis 230
sentiment confidence estimation 229–230
topic tagging 233
regular announcements 223–224
D
Degree centrality 159–160
Dianping’s filtering system 150–151
Digital media 14–15
Dimensional models, emotions 52–53
Discrete emotion models See Categorical models
Distant learning 39
Distributional semantics 41
Domain-dependent lexica 33
E
Eigenvector centrality 159
Emolex lexicon 35
EmoSenticNet 37
Emotional marketing 198
Emotion lexica 35–36
Emotion lexicon 62
Emotion Markup Language (EmotionML) 54–56
corpus of children’s tales 198–199
corpus of Japanese tweets 199
hourglass model 75–77
appraisal theories 53
categorical models 53
dimensional models 52–53
emotion corpus 60
emotion lexicon, lemon 62
EmotionML 56
vocabularies 55–56
polarity 198–199
SemEval campaigns, tasks on 199
SOMA See (SOMA)
Engagement metrics 17
Ensemble learning 97
European Semantic Web Conference 31
Expertise, novelty, influence, and activity (ENIA) 165
Explicit opinion 7
Explicit suggestions 132
crude features and user-defined feature 175
definition 171–172
opinion-filled text 173
Extrinsic summary evaluation 173–174
F
definition 113
irony detection See (Irony)
sarcasm detection See (Sarcasm detection)
sarcastic and ironic sentences 8–9
schemes and tropes 8
FinancialTwitterTracker 65–66
Friedkin-Johnsen influence model 163
G
Gaussian function 75
General Inquirer 33
Grice’s theory of irony 113
H
emotional void 75–77
Gaussian function 75
Minsky’s theory of mind 75
Hu and Liu system 174–175
Humor 226
I
Implicit opinion 7
Implicit suggestions 132
Influence metrics 18
InfluenceRank algorithm 164
Interoperability, language resources 49
Hu and Liu system 175
pyramid method 173
characteristic of 9
definition and theories 113
detection 240
classification approach 116
conceptual features 116
crowdsourcing 115
lexical features 116
objective of 115
real-time opinion streaming 226
self-tagging 115–116
textual features 115–116
two-stage model 116–117
examples of 8
polarity reversal 114
positive and negative terms, frequency of 114
Irrealis mood 231–232
Iterative classification algorithm (ICA) 152–153
J
Joint sentiment topic (JST) model 99
K
Kernel density estimation 142–143
Keyword spotting 73
life insurance organization 220–221
social media analytics 220f
codebook/coding framework 219
hand coding 219
random sampling 219
statistical predictive model 219
subgroup analysis 219
social media and sentiment philosophy 214–215
continuous learning 216–217
predictive models 218–219
pretesting 215
sampling 217–218
vision 213–214
L
LBP See Loopy belief propagation (LBP)
LeaderRank models 160
Lexical affinity 73
emotion lexicon 62
sentiment lexicons 61–62
Linghub project 66
Linguistic Inquiry and Word Count (LIWC) 36
goals of 50
PROV-O 50
sentiment corpus, NIF 57–60
sentiment lexicons, lemon 61–62
appraisal theories 53
categorical models 53
dimensional models 52–53
emotion corpus 60
emotion lexicon, lemon 62
EmotionML 56
vocabularies 55–56
Linguistic linked open data (LLOD) cloud 49
Linked open data (LOD) project 49
LOD cloud 49
Longitudinal user centered influence model 163
M
relationships and natural language 101f
Macquarie Semantic Orientation Lexicon 115–116
Markov process 163
Markov random fields (MRFs) 142–146
MaxDiff Twitter Sentiment Lexicon 34
Microblogging social networks 17
Minsky’s theory of mind 75
Movie Review Data 38–39
MPQA lexicon 61
MultiWordNet 38
N
Negation 41
NRC Emotion Lexicon 61
NRC Hashtag Affirmative Context Sentiment Lexicon 34
NRC Hashtag Negated Context Sentiment Lexicon 34
O
Online social networks 13–14
definition 14–15
digital revolution 91
features 16
history of 15–16
indexes and metrics 17–18
applications 103–104
dynamics 92
ethical principles 103
explicit and implicit information 92
intelligence applications 103
medical field, application in 103
multilingualism 92
noisy content 92
relationships 93
short messages 92
status homophily 93
value homophily 93
need for cognition 19
need to belong 18–19
self-presentation and impression management 19
homophily principle 20
sentiment analysis 23–25
social network analysis 21–22
source credibility 20–21
tie strength 20
star relationship 17
two way/friendship 17
user-generated content, types of 16–17
vanity metrics 13–14
Open Linguistics Working Group 49
OpinionFinder 32–33
OpinionFlow 181
challenge for 166–167
content mining 164–165
definition 157–158
interaction information and content 165–166
observations 168
personal competence 157–158
personification of certain values 157–158
strategic social location 157–158
strategies for 158
two-step flow of communication model 157–158
Opinion Lexicon 32–33
Opinion mining See Sentiment analysis (SA)
Opinion Observer 180–181
Opinion spammers 141
Opinion summarization 241
definition 172
noisy documents 172–173
opinion-filled text 173
product reviews 175–176
steps 172
definition 171–172
opinion-filled text 173
product reviews 175
extrinsic evaluation 173–174
grammatical documents 171–172
Hu and Liu system 174–175
news articles and scientific journals 171–172
nonweb sources 176
online product reviews 175–176
restaurant reviews 175–176
big data 184–185
customer feedback 178t
explore opinions on single entity 179–180
feature-based sentiment analysis 178
goal 178
multiple entities 180–181
design challenges 177
interactive techniques 177
uncertainty 184
P
PageRank models 159–160
Plutchik’s circumplex model 37–38
Polarity classification 3–4
applications 103–104
dynamics 92
explicit and implicit information 92
multilingualism 92
noisy content 92
relationships 93
short messages 92
relationships and natural language 101f
Polypathy 40–41
Positive opinion leader detection method 164
Positive-unlabeled (PU) learning 150
Predictive modeling 218–219
Probability theory 218
Profile-based social networks 16
Provenance 50
PROV Ontology (PROV-O) 50
Pyramid method 173
Q
QUOTUS 164–165
R
Relational sentiment analysis 230
Review burst detection 142–143
loopy belief propagation 146
Markov random fields 143–146
review burst detection 142–143
Review Spotlight 180
R language 194–195
S
Sankey diagram 181
Sarcasm detection 240
behavioral modeling approach 118
binary classification experiments 118
bootstrapping algorithm 118
characteristic of 9
crowdsourcing 115
ensemble approach 118
examples of 8
objective of 115
real-time opinion streaming 226
sarcastic author 119
semisupervised algorithm 117
Semantics 8
concept-level resources and ontology 37–38
distributional semantics 41
emotion lexica 35–36
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
bootstrap techniques 98
incremental learning 104
lexical-based approaches 98
future direction 105
status homophily 102
value homophily 102
Semisupervised subjective feature weighting and intelligent model selection (SWIMS) 96–97
Senpy 62
affective commonsense reasoning 71–72
hourglass of emotions model 75–77
social media marketing 79–82
social networks 72
SentiCircles 43
SenticNet 37
Sentilo 42–43
Sentiment140 Affirmative Context Lexicon 34
Sentiment analysis (SA) 198
applications 9–10
business intelligence 71
computational models 73
definition 71
industrial activities 2
knowledge-based systems 73
linguistic linked open data See (Linguistic linked open data)
online social networks See (Online social networks)
polarity classification See (Polarity classification)
semantics See (Semantics)
sentic computing See (Sentic computing)
SpagoBI See (SpagoBI)
statistical-based approaches 73–74
Sentiment lexicons 61–62
Sentiment140 NRC Twitter Sentiment Lexicon 61
Sentiment treebank 42
SentiSense 36
Smart Media Management for Social Customer Attention See SOMA
Smart social customer relationship management See SOMA
automated listening tools 213–214
automated tools 212
coding errors 213
companies and brands 211–212
content publishing 211
data-driven marketing 212
false sentiment data 212
KRC Research See (KRC Research)
sentic computing 79–82
smart analytics 213
language revolution 5
relational environments 5
friendship relationships 9
linguistic linked open data See (Linguistic linked open data)
online social networks See (Online social networks)
opinion spam detection See (Spam detection)
semantics See (Semantics)
sentic computing See (Sentic computing)
SpagoBI See (SpagoBI)
Social networks sites 15
Social Sandbox 215
objectives 197–198
Source credibility theory 20–21
data sources 189–190
documents 189–190
Mozilla Public License version 2.0 189
features 190–192
marketing campaign monitoring and analysis 192–193
naïve Bayes classification algorithm 194–195
purpose of 190
tasks 189
Spam detection 240
collective classification 152–153
evaluation 153–154
problem definition 150–152
early detection 154
language inconsistency 154
multiple site comparisons 154
nature of business 154
loopy belief propagation 146
Markov random fields 143–146
review burst detection 142–143
SPOT 141–142
SSDM 141–142
SYBILRANK 141–142
inference 149
opinion spam/spammer evaluation 149–150
T-MRF 147–149
Web use abnormality 154
SPARQL queries, sentiment lexicons 65
Status homophily relationships 93
semisupervised learning 102
supervised learning 100–101
unsupervised learning 102–103
Subjectivity Lexicon 32–33
Subjectivity Sense Annotations 33
Suggestion mining 240
advice mining 129–130
applications 136–137
definition 129
for improvements 135
product reviews 130
reviews 133
rule-based classifiers 134
statement classification task 131–132
statistical classifiers 134
task definition 131–132
wish detection problem 129
extrinsic evaluation 173–174
Hu and Liu system 175
pyramid method 173
Sum-product algorithm 146
ensemble learning 97
graphical representation 96f
implicit opinions 104
limitation 98
naïve Bayes and neural network classifiers 95–96
rule-based classifier 96–97
support vector machines 96–97
word embeddings 97
future direction 104
status homophily 100–101
value homophily 101–102
Supervised text classification 150
T
Tag cloud–based visualization 180
Terrorism 10
Text Encoding Initiative 49
Topic sentiment mixture model 99
TrendMiner 65–66
TwitInfo 181
definition 15–16
hashtag 15–16
SpagoBI Social Network Analysis module 190–192
inference 149
opinion spam/spammer evaluation 149–150
T-MRF 147–149
Typed Markov random fields (T-MRF) 147–149
U
Uniform resource identifiers (URIs) 56–58
components 103
generative models 99–100
lexicon-based methods 99
future direction 105
status homophily 102–103
value homophily 103
short and noisy text 104
V
Value homophily relationships 93
semisupervised learning 102
supervised learning 101–102
unsupervised learning 103
Vanity metrics 13–14
W
Word embeddings 41
WordNet 33–34
World Wide Web Consortium (W3C) 49
Y
Yelp 136–137
Yelp Lexicon 33
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