isMOOD: Listening to the customers’ voice through social network analytics

Listening to the customers’ voice through social network analytics Χρήστος Κουνάβης & Δρ. Διονύσιος Σωτηρόπουλος

15ο Συνέδριο InfoCom World

30/10/2013

ismoodcom [email protected] www.ismood.com

The services

Social Network Analytics

Listen to the customer Discover your market

Target your niche Explore emerging opportunities

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The services

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From data to knowledge


The services

Actionable Intelligence

Immediacy

Competitive Intelligence

Brand Management

Market monitoring

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Greek Case

ü  Example on Twitter Analysis

ü  Collecting Real Time twitter data (30/09/2013 – 12/10/2013)

ü  Keywords: cosmote, vodafone_gr, wind_hellas

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Greek Case: Sentiment Analysis

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Greek Case: Topic/Trend Detection

Topic #1

Keywords: πρωτοετείς, φοιτητές, προκήρυξη, υποτροφία

Tweets per day Sentiment per day

“Προκήρυξη Υποτροφιών OTE-COSMOTE: Δώδεκα χρόνια δίπλα στους πρωτοετείς φοιτητές: Είκοσι (20) υποτροφίες ύψους...

http://t.co/3o8ZEAxAC1”

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Topic #2

Keywords: γρήγορα, πόσο, ξενερώνει, χώρα


“Πιο γρήγορο από το 4G της COSMOTE είναι το πόσο γρήγορα σε ξενερώνει αυτή η χώρα....”

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Topic #3

Keywords: μόνο, γρήγορο, τελικά, πρωτογενές, νεοναζί


“Tο μόνο πιο γρήγορο από το 4G της COSMOTE είναι τελικά η κράτηση των νεοναζί... Και το πρωτογενές πλεόνασμα...”

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Topic #1

Keywords: κέρδισε, διπλές, προσκλήσεις, αγώνα, εθνικής


“Κέρδισε και εσύ διπλές προσκλήσεις για τον αγώνα της Εθνικής από το @sport24 & τη @Vodafone_GR”

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Topic #2

Keywords: iphone, καταστήματα, απίστευτο


“Φήμες θέλουνε το iPhone 5S στα ράφια των καταστημάτων της Vodafone, Παρασκευή 18/10”

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ismoodcom [email protected] www.ismood.com 13/29




Τrends not found!

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Greek Case: Knowledge based on the trends

Large network which discussed : Social activities, Advertisements

Small network which discussed : Social activities, New products

Limited network which discussed : Technical problems

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How?

ü  Sentiment Analysis at the document, sentence, and aspect level.

ü  Opinion Holder identification.

ü  Opinion mining (find trends, trustworthy opinions, spam opinions, and fake reviews).

ü  Social media analysis (Twitter, YouTube, Facebook, etc.) for product, brand, and people-related opinions.

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The tools

Beyond-SOTA machine learning algorithms providing superior performance and accuracy in:

ü  Topic and group modeling

ü  Text mining and summarization

ü  Sentiment classification

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US Case

ü  Example on Twitter Analysis

ü  Collecting Real Time twitter data (11/02/2013 – 22/02/2013)

ü  Keywords: at&t, verizon

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US Case: Sentiment Analysis

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US Case: Topic/Trend Detection

Topic #1

Keywords: commercial, kids, little, love, funny, new, guy


“Nothing in this world is as precious as the kids in the AT&T commercials. Nothing.”

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Topic #2

Keywords: free, wifi, cloud, hate, customers, deal, offer, network


“AT&T and WiFi provider The Cloud announce roaming agreement http://t.co/39fR8LQV #WiFi @ATT #TheCloud by

@nirave”

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Topic #3 Keywords: every, you, way, possible, single, person, dear, frustrated


“Dear AT&T I’m curious WHAT DID WE DO WRONG TO RECEIVE SHITTY SERVICE”

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US Case: Sentiment Analysis

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Topic #1

Keywords: phone, get, service, like, dont, fuck


“This is why everyone needs VERIZON ! You’ll get service everywhere !”

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Topic #2

Keywords: fios, lte, commercial, like, sale


“I love FIOS actually, nvr had any prob with it knock on wood.”

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US Case: Knowledge based on the trends

Network which discussed : Advertisements, Support, Products

Network which discussed : Products, Network Infrastructure

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Gaining Competitive Advantage with Advanced Intelligence Tools

“How satisfied is my market?”

“What does my market need and discuss?”

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Our team

Chris Kounavis

Founders

Anna Kasimati

Dionysios Sotiropoulos

George Giaglis

Konstantinos Fouskas

Academic Advisors

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Thank you!

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isMOOD: Listening to the customers’ voice through social network analytics

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