An Intro to Text Analytics on Big Data with a use case

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Raul Chong

Introduction on how to perform text analytics using input from twitter and the "Emmys" as use case example.

Technology

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Toronto SMAC Meetup – Welcome!
An Intro to Text Analytics on Big Data with a use case

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Toronto SMAC Team
| © 2014 IBM Corporation2
Lucas Silva Felipe MosquettaMarcos de
Mello

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Twitters numbers
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation3
As you know:
-500 million Tweets are sent per day.
-Twitter supports 35+ languages.
-255 million monthly active users.
Huge amount of data!

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An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation4
Overview
Section1 Section2 Section3 Section4 Section5

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Section1 Section2 Section3 Section4 Section5
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation5
Overview

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Section1 Section2 Section3 Section4 Section5
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation6
Overview

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Let’s get started!
| © 2014 IBM Corporation7

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Input data
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation8

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An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation9
Section2

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Section1 Section2 Section3 Section4 Section5
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation11
Next section

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Section1 Section2 Section3 Section4 Section5
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation12
Next section Extractor: used to extract
structured information from
unstructured and
semi-structured data.
AQL: Annotation Query
Language. Rule language
with familiar SQL-like syntax.

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Section1 Section2 Section3 Section4 Section5
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation13
Next section
Profiler:
troubleshooting performance
problems.

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation14
Types of extraction specifications:
- Dictionaries
- Regular expressions
- Part of speech

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation15

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation16

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An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation17

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation18

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation19

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation20
Types of extraction specifications:
- Dictionaries
-Regular expressions
- Part of speech
numbers:
7.5
4
13

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation22
Types of extraction specifications:
- Dictionaries
- Regular expressions
- Part of speech

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Main concepts
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation23

#TOSMAC
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation24

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| © 2014 IBM Corporation25
An Intro to Text Analytics on Big Data with a use case
AQL Guidelines
Basic feature AQL statements
- Develop the core building blocks of the extractor.

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| © 2014 IBM Corporation26
An Intro to Text Analytics on Big Data with a use case
AQL Guidelines
Candidate generation AQL statements
- Combine basic features AQL statements.

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| © 2014 IBM Corporation27
An Intro to Text Analytics on Big Data with a use case
Candidate generation AQL statements
$7.5 million
$4 thousand
$ 7.5 million

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| © 2014 IBM Corporation28
An Intro to Text Analytics on Big Data with a use case
Candidate generation AQL statements
$7.5 million
$4 thousand
$ 7.5 million
$7.5 million

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| © 2014 IBM Corporation29
An Intro to Text Analytics on Big Data with a use case
AQL Guidelines
Filter and consolidate AQL statements
- Refine results
- Remove invalid annotations
- Resolve overlap between annotations.

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| © 2014 IBM Corporation31
An Intro to Text Analytics on Big Data with a use case
Conclusion

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Check point
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation32

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What we have done
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation33
Section1 Section2 Section3

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What are we going to do?
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation34
Section4 Section5

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Also using R
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation36
1.75 0.32

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What are we going to do?
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation37

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So what?
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation39

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Companies
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation40

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Exporting to you
An Intro to Text Analytics on Big Data with a use case
| © 2014 IBM Corporation41

#TOSMAC
Thank you!
Let's network!
| © 2014 IBM Corporation42

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An Intro to Text Analytics on Big Data with a use case

1. #TOSMAC Toronto SMAC Meetup – Welcome! An Intro to Text Analytics on Big Data with a use case

3. #TOSMAC Twitters numbers An Intro to Text Analytics on Big Data with a use case | © 2014 IBM Corporation3 As you know: -500 million Tweets are sent per day. -Twitter supports 35+ languages. -255 million monthly active users. Huge amount of data!

12. #TOSMAC Section1 Section2 Section3 Section4 Section5 An Intro to Text Analytics on Big Data with a use case | © 2014 IBM Corporation12 Next section Extractor: used to extract structured information from unstructured and semi-structured data. AQL: Annotation Query Language. Rule language with familiar SQL-like syntax.

20. #TOSMAC Main concepts An Intro to Text Analytics on Big Data with a use case | © 2014 IBM Corporation20 Types of extraction specifications: - Dictionaries -Regular expressions - Part of speech numbers: 7.5 4 13

29. #TOSMAC | © 2014 IBM Corporation29 An Intro to Text Analytics on Big Data with a use case AQL Guidelines Filter and consolidate AQL statements - Refine results - Remove invalid annotations - Resolve overlap between annotations.

An Intro to Text Analytics on Big Data with a use case

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