Developing Smart Cities Services through Semantic Analysis of Social Streams

Developing Smart Cities Services
through Semantic Analysis
of Social Streams
Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops
(Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)
WDS4SC 2015
WWW 2015 Workshop on
Web Data Science and Smart Cities
Florence (Italy) - May 19, 2015

Outline
• Background
• Information Overload
• Social Content Analytics
• CrowdPulse
• Social Data Extraction
• Semantic Tagging
• Sentiment Analysis
• Processing & Visualization
• Use Cases
• L’Aquila Social Urban Network
• The Italian Hate Map
• Conclusions
2Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops
Developing Smart Cities Services through Semantic Analysis of Social Streams. WDS4SC 2015 Workshop, Florence (Italy) 19.05.2015

Background

Background
4
Information
Overload

Information Overload
5
… in digital life

6
… in real life

7
Obstacleor
Opportunity?

Background (again)

9
Social Networks
can be considered as novel data silos

10
Social Networks
information about preferences

11
Social Networks
information about connections

12
Social Networks
information about people feelings

13
Social Networks
changed the rule for content analytics

14
Social Content Analytics
Successful Use Cases
- Online brand
monitoring
- Social CRM
- Real-time polls
All these applications share a common
insight

15
Social Content Analytics
Research Question
Is it possible to aggregate rough human-generated
data to get complex people-based ﬁndings?

16
Our contribution: CrowdPulse
A framework for real-time
Semantic Analysis of Social Streams

17
CrowdPulse
Social Data Extraction
features
Semantic Tagging
Sentiment Analysis Processing & Visualization

workﬂow
CrowdPulse

Step 1: Social Data Extraction
CrowdPulse

Extraction
Source
Heuristics
CrowdPulse

Extraction
Source
Heuristics
Content
User
Geo
Content+Geo
#www2015
#democrats
#trafﬁc
@barack_obama
@comuneﬁ
#earthquake
Page
Group
CrowdPulse

Extraction
Source
Heuristics
Content
User
Geo
Content+Geo
#www2015
#democrats
#trafﬁc
@barack_obama
@comuneﬁ
#earthquake
Page
Group
We only extract public content
CrowdPulse

Step 2: Semantic Tagging
CrowdPulse

25
aquila
?
?
(eagle)
(italian city)
(italian)
Semantic Tagging
Motivations
Poor Semantics

Keyword-based representation introduces
a lot of noise in the analysis
26
aquila
?
?
(eagle)
(italian city)
(italian)
Semantic Tagging
Motivations

(Please, do something: l’Aquila is going to die!)
(Please, do something: the eagle is going to die!)
“Fate qualcosa per favore, l’Aquila sta morendo!”
?
27
Semantic Tagging
Motivations

• Entity Linking Algorithms
• Input: textual content
• Output: identiﬁcation and
disambiguation of the
entities mentioned in the text.
(1) http://tagme.di.unipi.it
(2) http://spotlight.dbpedia.org
28
Solution: semantic processing of extracted content
Algorithms
CrowdPulse

29
CrowdPulse
Entity Linking: identiﬁcation and disambiguation of the

30
CrowdPulse
Non-trivial NLP tasks (stopwords removal, n-grams identiﬁcation, named
entities recognition and disambiguation) are automatically performed

CrowdPulse
31
Each entity is a reference to a Wikipedia page
http://it.wikipedia.org/wiki/Massimo_Cialente
IMPORTANT!

We enriched the entity-based representation
by exploiting the Wikipedia categories’ tree
CrowdPulse

We enriched the entity-based representation
by exploiting the Wikipedia categories’ tree
CrowdPulse
Many interesting (new) features come into play!
(e.g. italian politics, L’Aquila mayors, Democrats politics)

The ﬁnal representation of each content is obtained by
merging the entities identiﬁed in the text with the most
relevant Wikipedia categories each entity is linked to.
Features = Entities + Wikipedia Categories
CrowdPulse

35
CrowdPulse
Step 3: Sentiment Analysis

36
Sentiment Analysis
Motivations
Is this content conveying any opinion?

37
Sentiment Analysis
Motivations
Is this content conveying any opinion?
This is a crucial issue if people-based ﬁndings have to be generated

38
Sentiment Analysis
Deﬁnition
“It is the ﬁeld of study that
analyzes people’s
opinions, sentiments,
evaluations, appraisals,
attitudes, and emotions
towards entities such as
products, services,
organizations, individuals,
issues, events, topics, and
their attributes “ (*)
(Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval, 2008)
We concentrated on the polarity detection task

39
Sentiment Analysis
How to develop an (unsupervised) sentiment analysis algorithm?

40
External lexical resources
associate a polarity score to each term.
joy +++
frustration - -
Sentiment Analysis
Lexicon

41
SenticNet(*)
(*) Cambria, Erik, Daniel Olsher, and Dheeraj Rajagopal.
"SenticNet 3: a common and common-sense knowledge
base for cognition-driven sentiment analysis." Twenty-
eighth AAAI conference on artificial intelligence. 2014.
Inspired by the Hourglass of
Emotions model
Each term is represented on the
ground of the intensity of four basic
emotional dimensions (sensitivity,
aptitude, attention, pleasantness)
The activation level of each dimension
deﬁnes 16 basic emotions
Sentiment Analysis

42
Sentiment Analysis
SenticNet
According to the triggered emotions, each term
is provided with an aggregated polarity score

43
SenticNet
SenticNet models a sentiment score
for some bigrams and trigrams as well!
Sentiment Analysis

44
Insight:
The polarity of a textual content (e.g. a
microblog posts) depends on the polarity
of the microphrases which compose it.
Sentiment Analysis
Methodology

45
Insight:
A microphrase is built
whenever a splitting cue
is found in the text
Sentiment Analysis
Methodology

46
Insight:
Conjunctions, adverbs and
punctuations are used as
splitting cues
Sentiment Analysis
Methodology

47
Insight:
splitting cues
example: “I don’t like this food, it’s terrible”
Sentiment Analysis
Methodology

48
Insight:
splitting cues
example: “I don’t like this food, it’s terrible”
{
{
m1 m2
splitting
cue
Sentiment Analysis
Methodology

49
Insight:
pol(C) = ∑ pol(mi)
i=1
k
Content microphrase
T={m1…mk}
Sentiment Analysis
Methodology

50
Insight:
pol(T) = ∑ pol(mi)
i=1
k
The polarity of a content depends on the
polarity of the micro-phrases which
compose it.
pol(mi) = ∑ score(tj)
j=1
term
n
T={m1…mk}
Mi={t1…tn}
Content microphrase
Sentiment Analysis
Methodology

51
Insight:
pol(T) = ∑ pol(mi)
i=1
k
The polarity of a microphrase depends on
the polarity of the terms which compose it.
pol(mi) = ∑ score(tj)
j=1
term
n
T={m1…mk}
Mi={t1…tn}
Tweet microphrase
Sentiment Analysis
Methodology

52
CrowdPulse

53
CrowdPulse
Overall sentiment: :-(

54
CrowdPulse
The process can be iterated over a larger set of
content, to get ﬁndings about the feeling of the
population regards a certain topic

55
CrowdPulse

56
CrowdPulse
Step 4: Processing & Visualization

57
CrowdPulse
Step 4: Domain-speciﬁc processing
Supervised learning
Unsupervised learning
Linguistic Analysis
classiﬁcation, regression tasks
clustering
building word spaces, similarity between
concepts, analysis of terms usage, etc.

58
CrowdPulse
Supervised learning
Linguistic Analysis
clustering
CrowdPulse natively supports all these methodologies

59
CrowdPulse
Supervised learning
Linguistic Analysis
clustering
The choice is typically scenario-dependent

60
CrowdPulse
Step 4: Data Visualization
An interactive analytics
console is made available for
each project
Descriptive statistics can be
built in real-time and can be
immediately shown

61
CrowdPulse
Recap

62
Use Cases

L’Aquila Social Urban Network The Italian Hate Map
1. 2.
Use Cases

Use Cases
L’Aquila Social Urban Network
April 6, 2009
5.8 magnitude earthquake
20 billions damages
70,000 people displaced
309 people died

Use Cases
2015: six years later
7 billions fundings still needed
22,000 people still displaced
Diaspora

Use Cases
19 ‘new towns’ around l’Aquila 15,200 people today live there

Use Cases
What about the consequences?
Loss of trust, sense of belonging, relationships

Use Cases
Loss of social capital

Use Cases
Research Question:
Is it possible to extract and process social
media to monitor in real time people feelings,
opinions and sentiments about the current
state of the social capital of L’Aquila?

Use Cases
Research Question:
We can use CrowdPulse
Is it possible to extract and process social
media to monitor in real time people feelings,
opinions and sentiments about the current
state of the social capital of L’Aquila?

Use Cases
Heuristics:
- Twitter users (local newspapers, mention to politicians)
- Twitter content+geo (50km around l’Aquila with speciﬁc
hashtags as #laquila #earthquake, etc)
CROWDPULSE SETTINGS

Use Cases
CROWDPULSE SETTINGS
Heuristics:
- Facebook groups (identiﬁed after a thorough analysis)
- Facebook pages (identiﬁed after a thorough analysis)

Use Cases
Extracted content (example)
Tweets about the fear of new earthquakes.
Facebook posts about citizens’ proposals.
Tweets about people worried of the situation.
Tweets about new buildings in the city.
CROWDPULSE SETTINGS

Use Cases
Sentiment Analysis and Semantic Tagging of the content
CROWDPULSE SETTINGS

Use Cases
How to map each content with the
social indicator it refers to?
CROWDPULSE SETTINGS

Use Cases
Given a ﬁxed set of social capital indicators, we built a
classiﬁcation model to associate each content (along with
its sentiment) to the social indicator it refers to.
CROWDPULSE SETTINGS

Use Cases
Tweet about new buildings in the city.
Input: Social indicators + semantic
representation of the content

Use Cases
Domain-speciﬁc processing: Classiﬁcation model

Use Cases
Output: (multi-class) classiﬁcation + sentiment

Use Cases
The score of a social indicator is the average
sentiment of all the content referring to it.

Use Cases
CROWDPULSE OUTPUT
Overall score of the social indicators between March and August 2014

Use Cases
CROWDPULSE OUTPUT
COMMUNITY PROMOTER
DEFINES SOME INITIATIVES TO EMPOWER THE SOCIAL CAPITAL
MONITORS THE STATE OF THE SOCIAL INDICATORS
Real-world application
of the output

Conclusions
Crowdsourcing-based approach
Social content about
L’Aquila are extracted and
processed in real-time
Machine Learning
exploited to build a classiﬁcation
model
Sentiment Analysis
used to provide each social
indicator with a score
1. 2.
3. 4. Analytics Console used
to monitor the state of the social
capital in real-time
Almost 500,000 social content extracted and analyzed.

Use Cases
The Italian Hate Map
http://users.humboldt.edu/mstephens/hate/hate_map.html
Inspired by the
Hate Map built by
the Humboldt
University
joint research with a
psychologists team of
Rome University and a
no-proﬁt agency
focused on human
rights

Use Cases
http://users.humboldt.edu/mstephens/hate/hate_map.html
Insight:
To aggregate rough
people-based data
in order to analyze
complex
phenomena.

Use Cases
Research Question:
Is it possible to extract and process social media
to detect intolerant content posted on social
networks and identify the most at-risk areas of the
Italian country?

Use Cases
Research Question:
Is it possible to extract and process social media
to detect intolerant content posted on social
networks and identify the most at-risk areas of the
Italian country?
We can use CrowdPulse

Use Cases
Heuristics: Twitter content
- 76 intolerant seed terms, deﬁned by the psychologists teams
- 5 intolerance dimensions: violence (against women), racism,
homophobia, disability, anti-semitism
CROWDPULSE SETTINGS

Use Cases
Extracted content (seed term: nano/midget)
Tweet about an Italian ministry
CROWDPULSE SETTINGS
Tweet about iPod nano
Tweet about an Italian football player

Use Cases
Many non-intolerant Tweets are extracted!
Tweet about an Italian ministry
CROWDPULSE SETTINGS
Tweet about iPod nano
Tweet about an Italian football player
X
X

Use Cases
Non-intolerant Tweets are detected and ﬁltered out.
CROWDPULSE SETTINGS

Use Cases
Ironic Tweets are detected and ﬁltered out.
CROWDPULSE SETTINGS

Use Cases
CROWDPULSE SETTINGS

Use Cases
We have to build a map, so we
only need geotagged content
CROWDPULSE SETTINGS

Use Cases
CROWDPULSE SETTINGS
Deﬁnition of heuristics to increase the
number of geotagged Tweets

Use Cases
Dimension #Tweets #Geo %Geo
Homophobia 110,774 8,501 7,66%
Racism 154,170 1,940 1,24%
Violence 1,102,494 28,886 2,62%
Disability 479,654 3,410 0,75%
Anti-Semitism 6,000 1,150 18,03%

Use Cases
CROWDPULSE OUTPUT
Violence against women Disability
Racism Homophobia

Use Cases
CROWDPULSE OUTPUT
Given the maps and given the output of the linguistic
analysis of intolerant Tweets (co-occurrences between terms,
timelapse, etc.), the psychologists team deﬁned some
guidelines to tackle and prevent intolerant behaviors.
These guidelines have been freely distributed to public
administration on early 2015.

Conclusions
Crowdsourcing-based approach
Social content
containing the seed terms is
extracted and processed in
real-time
Semantic Processing
exploited to delete non-intolerant
Tweets
Sentiment Analysis
used to ﬁlter out Tweet
with irony
1. 2.
3. 4. Analytics Console used
to build real-time hate
maps
Almost 2,000,000 social content extracted and analyzed.

Lessons Learned

Lessons Learned
102
Pipeline of state of the art techniques
Entity Linking, Sentiment Analysis, Machine Learning, Data Visualization
Use Cases.
DEFINITION OF A FRAMEWORK FOR
REAL-TIME SEMANTIC CONTENT ANALYSIS
1.
2.

Lessons Learned
103
Pipeline of state of the art techniques
Entity Linking, Sentiment Analysis, Machine Learning, Data Visualization
Use Cases.
DEFINITION OF A FRAMEWORK FOR
REAL-TIME SEMANTIC CONTENT ANALYSIS
1.
2.
The outcomes of both use cases showed that very
complex phenomena can be analyzed in a totally new
way, thanks to the huge availability of textual data

Future Research
104
Integration of further machine learning
techniques, and further data visualization
formalisms
Evaluation of the real impact of the framework
in real-world dynamics (e.g., do intolerant
behaviors decrease thanks to the Hate Map?)
Improvement of the algorithms
for semantic tagging,
text classiﬁcation and sentiment analysis

questions?
Cataldo Musto, Ph.D
cataldo.musto@uniba.it
@cataldomusto

Developing Smart Cities Services through Semantic Analysis of Social Streams

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