Sentiment Analysis on Social Network

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SentimentAnalysisonSocialNetwork
ConferencePaper·February2016
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3authors,including:
VitoSantarcangelo
GiuseppeOddo
CentroStudi,Buccino(SA),Italy
CentroStudi,Buccino(SA),Italy
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Availablefrom:VitoSantarcangelo
Retrievedon:26July2016
Sentiment Analysis on Social Network
a cura di Ing. Vito Santarcangelo,
Dott. Antonio Ruoto e Ing. Giuseppe Oddo
Matera, 06/02/2016
Convegno “Ingegneria del Sentimento”
OSINT
United States Department of Defense defines the Open
Source Intelligence (OSINT) as “The intelligence discipline
that pertains to intelligence produced from publicly available
information that is collected, exploited, and disseminated in
a timely manner to an appropriate audience for the purpose
of addressing a specific intelligence and information
requirement”.
BIG DATA
Big data means large and heterogeneous
datasets obtained from web and local systems.
INFORMATION AND OPINIONS

Blogs, social networks and social
communities represent the mainly used
tools for information and opinions
sharing.
OPINION MINING

OSINT is also strictly related to Opinion
Mining (also known as Sentiment
Analysis), a discipline aiming at
retrieving the opinion of a subject
from web contents, reaching also the
reputation analysis scope.
OPINION MINING SYSTEM
STEPS:
 Target Definition (1)
 OSINT Extraction (2)
 Sentiment Analysis (3)
 Score Return (4)
After choosing a keyword/phrase (1), a
crawler extracts contents related to
the user input from OSINT data (2).
Sentiment Analysis (3) examines(2)the
polarity of the filtered data extracted
thanks to the use of a Sentimental
Thesaurus, associating a polarity
(negative, neutral, positive) for each
term of the extracted excerpts,
determining the Score (4).
KEYWORD (1)
OSINT
EXTRACTION (2)
EXCERPTS
SENTIMENT
ANALYSIS (3)
(3)
SCORE (4)
DB
FiCloud 2015
AIN APPROACH
ADJECTIVES (A), INTENSIFIERS (I) and NEGATIONS (N)
AIN APPROACH – FILTERING PHASE
If a tweet does not
contain one or more
adjectives/adverbs of our
thesaurus, it is dropped
from the tweet array.
AIN - ITALIAN THESAURUS
-1
-2
-1
1
AIN
THESAURUS
AIN LOGIC
AIN
THESAURUS
EXCERPTS
NON
MOLTO
DECADENTE
-1
-1
-2
0,5
-1
AIN + SEMANTIC - ITALIAN THESAURUS
F,E
FP,
A
FP,
L
F
AIN
SEMANTIC
THESAURUS
AIN THESAURUS (BOOK)
In press by RCE MULTIMEDIA
Graphic developed by Antonio Ruoto
www.ainthesaurus.it
HOW THE SYSTEM WORKS
USER
INPUT
WEB
CRAWLER
AIN
THESAURUS
+
SEMANTIC
NETWORK
SENTIMENT
ANALYSIS
SYSTEM
REPUTATION
OUTPUT
PARSER
TEXT ANALYZER
DATA
EXTRACTION
COMPARISON WITH SENTISTRENGHT
COMPARISON WITH SENTISTRENGHT
AIN FEATURES:
NON
MOLTO
DECADENTE
-1
-1
-USE OF NEGATION
COMBINED WITH
INTENSIFIER
-USE OF SEMANTIC
APPROACH
-2
0,5
-USE OF THREE METRICS
(DOMAIN EXPERT, COMMON
USER, AVERAGE)
-1
INSTAGRAM
SOCIAL NETWORK USAGE
HASHTAG QUERY
HASHTAG QUERY
USAGE OF INSTAGRAM
AIN ON INSTAGRAM

64,283,404 posts about adjectives
(30/01/2016)

Usage of positive adjectives : 46% of posts
Thanks to Michele Di Lecce
for the support in the extraction
Sentiment Hashtag Distribution
Instagram as Positive Egocentric Social Network
based on photos
Semantic Hashtag Distribution
Instagram as Positive Egocentric Social Network
based on photos
Figurative
People
Art
Most used Hashtags
Bello 2,020,475
VS
Brutto 82,391
Triste 1,028,314
VS
Felice 599,446
Nero 935,425
VS
Bianco 675,072
Instagram as Positive Egocentric Social Network
based on photos
SOME POSSIBLE APPLICATIONS:
REPUTATION ANALYSIS,
CUSTOMER SATISFACTION SYSTEM

Sentiment Analysis on social networks could
improve the service quality level of public
administrations, as it referred to people
opinions of the web.

It can be also useful to obtain the reputation
score about a person, an office, a project,
subject, theme of Public Administration
realizing an indirect customer satisfaction
system.

The integration with public opinions of
websites and blogs gives relevancy, accuracy
and quality to this kind of applications.
EXAMPLE OF APPLICATION
The user feedbacks can be integrated
with the reputation analysis of OSINT DATA
(as the App “Erice Amico Comune”
of “Comune di Erice”).
CONCLUSION & FUTURE WORKS

An improvement in ambiguity
management (e.g. “caro” that means
dear and expensive) could be obtained
through the examination of the
context using a semantic network.

The font style (bold, italic) and the
colour (red, black) of the text could
also be an input to evaluate.

The analysis of visual objects could
indicate the feelings of the users (e.g.
using the keyword Favignana, the
recognition of the sea in the photo
communicates good feelings, instead of
cockroaches near the hotel that
communicate bad feelings).
Vs
REFERENCES
For more information
and dataset visit
http://www.researchgate.net/profile/Vito_Santarcangelo
Thanks for the attention!