Social Media Text Analytics: Mining Value From Predictive Insights

SOC A M A ANA CSSOCIAL MEDIA TEXT ANALYTICSMINING VALUE FROM PREDICTIVE INSIGHTS

John Blossom, President, Shore Communications Inc.24 April 2012

Topicsp

“The Flood” – Drowning or Swimming in Social Media?

1

The Flood Drowning or Swimming in Social Media? Predictive Analytics – Understanding Focus & Intent Case Study – Sentibet.com Case Study Sentibet.com Lessons Learned

24 April 2012

Copyright © 1999-2012 Shore Communications Inc. - All Rights Reserved

Th Fl dThe Flood2

24 April 2012


The Flood: Global3

24 April 2012


The Flood: Real-Time, All the Time,

12,233 tweets/second

4

12,233 tweets/secondfor NFL SuperBowl

vs. NYSE record: vs. NYSE record:121,257 messages/sec.(5 August 2011)

As in finance, the value of social media analysis isshifting towards seeing shifting towards seeing patterns that enable the forecasting of intents

24 April 2012


The Flood: Adapting to Complexityp g p y5

Complex Word Analysis

P di ti M k

Simple/No Complex

Predictive Analytics

Market Sentiment

Simple/No Grammar/

lexicographic Analysis

Complex Grammar/

lexicographic Analysis

Events Triggers

Alerts Streams

24 April 2012


Simple Terms Matching

The Flood: The Social Media Gapp

Alerts

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Alerts Search filters via search engines, platforms & tags

Events Triggers Events Triggers Dow Jones Enterprise, InsideView

Market Sentiment Market Sentiment Radian6, Attensity, etc.

Predictive Analytics Predictive Analytics Early days, most focused on statistical analysis of terms

clustering clustering

24 April 2012


P di i A l iPredictive Analytics7

24 April 2012


Predictive Analytics and the Floody

Learning to swim in data, not drown in it

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Learning to swim in data, not drown in it Infer a focus from which you can infer intent

Identify the focus of specific cohorts Identify the focus of specific cohorts Use to forecast intents based on lexicographic analysis

24 April 2012


Making Predictive Analytics Workg y

Understand both:

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Understand both: Vocabulary Complete grammar/syntax Cohort Complete grammar/syntax

Understand it specific to: Cohorts Cohorts LanguageGeography/dialect

PlatformTopic

Interests

Topics Platforms Terms, jargons, idioms

24 April 2012


Formats, handles, syntax

Forecasting from Social Mediag

Predictive analytics

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The more sophisticated Predictive analytics enables pre-hypothesis testing of intents based

The more sophisticated the analysis, the more accurate the insights g

on semantically extracted sentiment &

gfrom smaller samples, enabling analysis of

forecasting statements highly focused cohorts

24 April 2012


C S d S ibCase Study: Sentibet.com11

24 April 2012


Sentibet.com – Forecasting Sportsg p

By Neurolingo, L.P.

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By Neurolingo, L.P. Complex lexicographic

analysis of social mediaanalysis of social media Yields event-specific

forecasts based onforecasts based onfan message analysis

Identifies, feelings, wishes Identifies, feelings, wishesand predictions

Applies proprietary formula for overall forecast Applies proprietary formula for overall forecast

24 April 2012


Sentibet™ – how it worksBlogs Social

NetworkingFiles, DBs, Reports

Subscription Content

Web Content

Sentibet™ pulls content from any available source via Neurolingo platform

Extracted Information are Extracted Information are further processed and analyzed to d

Sporting event data are identified, extracted, combined

together, verified, sorted and categorized

further processed and analyzed to determine:• Forecast Type (e.g. Home, Draw, Away etc.)

• Forecast Details (e.g. Correct Score etc.)

• Sentiment Type (e.g.

determine:• Reposted information• Discussion information (e.g. reply to a former post etc.)

• Type of information (e.g. opinion, press releases etc.)

• Source Details (e.g.

Sporting event sentiment/opinion information

are identified and extracted

Prediction, Feeling, Wish etc.) demographics etc.)• Fan Details

Confidence Rate is applied, based on the extracted information and domain

specific rules

Sentiment Based Forecasting algorithm produces the final forecasting figures

Neurolingo Mnemosyne™ platform process & analyze content based on Sporting Language patterns (“Kanon” rules) & Sporting Specific & General language

resources


Sentiment Based Forecasting algorithm produces the final forecasting figures

Analyses Topics, Cohorts, Platformsy p , ,

Event specific messages

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Event-specific messages Parses Twitter jargon

Cl b ifi d Club-specific terms and jargonS t t ifi Sports outcome specific language and termsF ll t i d Full stemming and parsing of combined grammarsgrammars

24 April 2012


Accurate Parsing of Smaller Cohortsg

Accurate extraction of forecasting messages from

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Accurate extraction of forecasting messages from smaller cohorts aids developing statistical significance

Weighting of user accuracy aids forecasts Weighting of user accuracy aids forecasts

24 April 2012


Social Media Provides Volume

Parsing Accuracy + Volume

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Parsing Accuracy + Volume = Statistically significant results

24 April 2012


Sentibet vs. Actual Outcomes17

Forecasted wins: Forecasted wins: 2012

Super Bowl XLVI(92,000+ forecasting tweets)

2012 NCAA Basketball Finals(3,900+ forecasting tweets)

Highly predictive g y pongoing forecasts for Premier League and Championship League Football

24 April 2012


L b L dLessons to be Learned18

24 April 2012


Sentiment vs. Forecastingg

What you want is not

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2012 NCAA Finals Kentucky (H) vs Kansas (A) What you want is not always what you thinkor feel will happen!

2012 NCAA Finals - Kentucky (H) vs. Kansas (A)

pp Dangers in sentiment

analysis based onlyy yon emotion gauging

Focus on the rightmessages to get statistically significant forecasts Understanding complex grammars the key to success

24 April 2012


In-House (Build) vs. Vendor (Buy)?( ) ( y)

Powerful lexicographic analysis requires special

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Powerful lexicographic analysis requires special tools

Maintenance required for shifts in vocabularies, Maintenance required for shifts in vocabularies, jargons & grammars of cohorts, topics & platforms

Argues for outsourcing extraction and semantic Argues for outsourcing extraction and semantic analytics backbone platform and in-sourcing apps

24 April 2012


Applicability for Many Sectorspp y y

Finance

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Trading triggers based on data mining

Draw conclusions from stacks of reports

Sports Enrich social media offerings and offer improved odds-making input

Target team match-ups for media markets Target team match ups for media markets

Politics Interpret voter intents more accurately

Determine more rapidly when to adjust strategies

Health I t t h d di ti f id ti Interpret research and diagnostics for rapid response actions

24 April 2012


For Follow-Up…p

PHONE (+01) 203.293.8511

EMAIL [email protected]

WEB shore.com contentblogger.com secondwebbook.com

TWITTER/GOOGLE+ @jblossom / John BlossomPOST POST John Blossom

PresidentShore Communications Inc.4 Merritt LaneW t t CT 06880 USA

24 April 2012

Copyright © 1999-2010 Shore Communications Inc. ALL RIGHTS RESERVED 22

Westport, CT 06880 USA

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Social Media Text Analytics: Mining Value From Predictive Insights

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