Transcript
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Innovating and Being Creative

with

1st Riyadh UseR Meetup15th December 2016Allure Hub, King Fahd Road

: https://goo.gl/O4rnv3: https://goo.gl/gx0iWX

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Ali Kazmi

http://goo.gl\IcwGiB

https://goo.gl/eBGEKd

@scac1041

https://goo.gl/tiWAMm

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• Meeting

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• Meeting• UseR Group

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• Meeting• UseR Group• Meetup Group

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• Meeting• UseR Group• Meetup Group

• 1st things 1st…

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Objectives

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Objectives

Promote Usage of R• Statistical Data Analysis

Tool• General Purpose

Programming Tool

Promote Computational Thinking

Promote Creativity with R

Enable Riyadh useRs to become ‘Data Analytic Citizens’

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Objectives

Promote Usage of R• Statistical Data Analysis

Tool• General Purpose

Programming Tool

Promote Computational Thinking

Promote Creativity with R

Enable Riyadh useRs to become ‘Data Analytic Residents’

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Content Coverage

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Content Coverage

• Commercial Settings– Use cases for Commercial work

• Personal Settings– Use cases for possibly non-Commercial/Private

work

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Structure of UseR Meetup Team

1. Ali Kazmi (Organiser)2. _________3. _________

Not a one man show, please.

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Today’s Presentations

• Personal Setting– Data Journalism with R and Stylometry: Identifying

number of writers for a Prime Minister's speeches

• Commercial Setting– Data de-duplication: Analysing misspelled names

to identify which refer to the same person

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Using Stylometry to Identify Authorship of Texts

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A series of events prompt the Pakistani Prime Minister to address the nation…

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A speech is delivered...

And, thereafter, an Audio clip is leaked, showing the PM taking advice on writing style

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Journalists wondered if the PM takes advice on writing style for important speeches only….

…Are some other speeches also a product of such brainstorming sessions?

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Media wondered if the PM takes advice on writing style for important speeches only….

…Are some other speeches also a product of such brainstorming sessions?

How can we answer this?

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Media wondered if the PM takes advice on writing style for important speeches only….

…Are some other speeches also a product of such brainstorming sessions?

How can we answer this?

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Stylometry is Linguistics + Statistics applied to detect stylistic changes in text

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Stylometry is Linguistics + Statistics applied to detect stylistic changes in text

Assumption of Stylometry: Each writer has a distinct style of writing that is unconsciously learnt and used.

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Various aspects of text can capture Stylistic variation:

• Punctuation Markers

• Length of a sentence

• Vocabulary Richness

• Parts of Speech

• Function Words

; , . !

Actually I don’t think that it is good because of the fact that this is not the…

It behoves me to accomplish this work.

Verb, Noun, Adjective, Adverb, Conjunction, etc.

That, but, therefore, and, etc.

What characterises a person’s writing style?

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Applications

• J. K. Rowling & Galbraith

• Writing Style in Novels

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Roadmap

• Extract

• Quantify

• Analyse

• Visualise

Multi-Dimensional Scaling, PCA, Bootstrap Consensus Trees

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Traditional Journalism vs. Data Journalism

• Traditional Journalism

• Data Journalism

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Considerations in Stylometry

• Size of dataset/corpus

• Open World Problem

• Relatively new field

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Questions?

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Data De-duplication: Analysing misspelled names to identify which refer to the same person

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Client approaches us for analysing transactional data with reference to contact names

1

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Client approaches us for analysing transactional data with reference to contact names

1

2Typos, variation in names…

Hamza Sheikh vs. Humza Shaikh vs. Hamza Sheik vs. Hazma Shiekh

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Client approaches us for analysing transactional data with ref. to contacts

1

2

Typos, variation in names…

- Hundreds of Thousands of records - 5 Days

What to do?

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Problem and Solution Elicitation

• Pattern of ‘errors’– Typing Mistakes– Minor Displacement of letters

• Solution– Pattern Matching ~ Risky, Time-consuming– String Matching Algorithms

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String Matching Algorithms

• stringdist package in R

• Edit-based distance measures– Includes:

• Deletion • Addition• Substitution • Transposition

– Generally: • Edit a string,• count iterations of edit• Less iterations = less distance = similar names!

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Examples of Edit-Based Measures

How many Insertions to obtain a particular text?

Duba Duba➜ i

How many Substitutions to obtain a particular text?

Tony ➜ Rony

How many Deletions to obtain a particular text

Swisss Swiss ➜

How many Transpositions to obtain a particular text?

Toyn To➜ ny

Greater the amount of edits to text, greater the dissimilarity of two text strings

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String Similarity MetricsSimilarity Metric Substitution Deletion Insertion Transposition

Longest Common Substring

Levenshtein

Damerau – Levenshtein

Jaro – Winkler

Soundex NA NA NA NA

Jaro – Winkler is a heuristic measure for typos. Designed to implement penalty if characters at remote positions are changed, as these are probably not typos – they occur due to transpositions at similar positions in a string.

Talha vs. Tahla Talha vs. Lahaat

Soundex checks phonetic similarity for English words.

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Application & Results

Similarity measures applied to relevant

columns

Using each similarity measure, records with the highest similarity

identified as duplicates and merged

4,243 unique donors found!

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• Can be quite expensive!– Memory insufficiency (with R)– Computationally time-consuming

Consideration

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Questions?

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• Stylometry for Data Journalism– Actual Study– Short Presentation

• Names’ De-duplication– Confidential

Links to Presented Work

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Should you like to Network now: Go ahead!

Otherwise: Thanks for joining this session!

Networking & Conclusion


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