How to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi

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How to use Latent Semantic Analysis to Glean Real Insight Better insights, Better content, Better engagement

Franco Amalfi Director, Digital Engagement Strategy Program Oracle Public Sector North America 23 May 2015

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Delivering Service to Citizens is Changing

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1980s 1990s 2000s 2010s

Empowered Employees

Digital is Humanized

Knowledge Everywhere

Internet of Things

Mobile as Primary Channel

Cross-Channel Service

WHAT’S NEXT

what’s

CHANGED 4


Empowered Citizens

5

Always Connected, Always Sharing, Always Aware

COMMUNICATION HAS CHANGED

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Social is a Key Component of a Citizen Engagement Strategy

Public Opinion Understand sentiment on key issues

on an ongoing basis

Citizen Engagement Interact with constituents on social networks to reach them rapidly and

at a lower cost

Transparency & Collaboration Connect with constituents on key issues to better gauge needs and

align strategy

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Average Time Spent on Social Media: 2.1 Hours



Challenges in the Modern Social Environment

Finding right communication channels 4

Citizens in multiple channels 2

Citizens posting problems publicly 3

Too much noise in social media 1



Oracle Social Relationship Management

Internal Social Collaboration

Social Engagement and Listening

Social Data

Social Publishing

Better Insights, Better Content, Faster Service

Leverage Oracle Social to empower your agency, enhance your CX capabilities and grow your business.

A unified social solution providing best-in-class networking, listening, engagement and publishing that can socially enable the enterprise.

Set of prebuilt integrations for social listening and engagement, social marketing, social commerce, social selling and social customer service.



Typical Social Listening Challenges

Spam and duplicate posts 1

Large conversation volumes 3

Search term ambiguity 2

Finding value in posts 4

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Oracle Uses a Blended Technology Approach for Listening Unstructured Text Processing


Latent Semantic Analysis

Natural Language Processing

Keyword Boolean

Terms plus Match Any, Match All, Match None conditions

Parsing unstructured text to diagram speech

Used in setting up Topics through Themes, and in categorization to: Topics & Indicators


Blended Listening Technology Approach Comparison


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Keyword Only Keyword Boolean KB & SemanticContext

Noise

Missed Valid

Valid

• Keyword Only overstates by 800%

• Keyword Boolean misses 40%

• Semantic Search – achieves most valid content


What I want to get posts about What I will get posts about in a keyword solution

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Example: Search Social Media for ‘Jaguar’

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Keyword Solutions Use Boolean Logic to Try to Find Relevant Posts

AND

(car OR drive OR driving OR automobile OR buy OR buying OR bought OR purchase OR purchasing OR purchased OR dealer OR dealership OR cars OR want OR …)

NOT

(football OR jacksonville OR stadium OR team OR animal OR jungle OR zoo OR lions OR tigers OR bears OR dating OR coach OR player OR players OR …)

JAGUAR

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PROS CONS

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Keyword Solution Using Boolean Search Pros & Cons

+ Tends to return results quickly

+ Quick on-boarding process

- Topics time consuming to build

- Must read results to find irrelevant posts

- No advanced analytics unless outsourced to 3rd party

- Time consuming & no consistency

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A data dictionary is built to increase accuracy

Common Advanced Analytics – Term Clouds

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How Natural Language Processing is Better than Keyword

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When all these things… … Mean this:

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What Natural Language Processing Has Difficulty Determining

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Doctor

Physician Pediatrician

Surgeon General

Practitioner


PROS CONS

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Natural Language Processing Pros & Cons

+ More relevant results

+ Advanced analytics via term clouds

+ More accurate sentiment

- Slow processing speeds

- Lengthy on-boarding time (months vs. days)

- Doesn’t automatically know when multiple words have same meaning

- Time consuming & expensive

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How Does Latent Semantic Analysis Work? Uses a statistically based algorithm that automatically groups conversations together that look alike. Or more simply put…



Occurrence Matrix



Processed Jaguar Conversations

Jaguar post

Jaguar post

Jaguar post

Jaguar post

Jaguar post

Jaguar post

Jaguar

Cars Drive

Buy / loan

Jungle Animals

Hunt

Football

Soccer

Game

Jaguar

Cars Drive

Buy / loan

Jungle Animals

Hunt

Football

Soccer

Game

Jaguar

Cars Drive

Buy / loan

Jungle Animals

Hunt

Football

Soccer

Game

Jaguar

Sedan

Buick

Mustang

Vehicle Coup

Wheel

Jaguar Cars


LSA Technology Finding & Targeting

the right conversations in all

the noise

92% Accurate Categorization


LSA Snippets only show relevant text around target word

Advantage = 1. More accurate sentiment 2. Thematic analysis 3. Indicators 4. Categorization


PROS CONS

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Latent Semantic Analysis Pros & Cons

+ Automatic categorization + 92% accurate categorization + Advanced analytics via themes & term clouds + White space analysis + Indicators for insight discovery + More accurate sentiment + Fast processing speeds + Easy on-boarding process

- None

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Summary Snapshot Comparison

Capability Keyword / Boolean Natural Language Processing Latent Semantic Analysis

Rapid set-up of listening topics 4 2 4 Listening accuracy; ease of

removing irrelevant results 1 3 4

Sentiment analysis without manual

effort 0 1 3

Indicators 0 0 4

Advanced analytics 0 4 4

Speed of run-time execution 4 2 4



Best Results Are Achieved When You Use All Three Oracle SRM Does



Blended Technology Approach

High categorization accuracy 92% Reduced cost

1 Set up a topic with a single keyword

Faster time to insight


Better Results and Ease of Use

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DEMO

Latent Semantic Analysis in Action

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Contact Us

Franco Amalfi

Email: [email protected]

@amalfifranco


https://www.linkedin.com/in/francoamalfi

mailto:[email protected]

https://twitter.com/amalfifranco

https://twitter.com/amalfifranco

