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GETTING FROM RAW DATATO CONTENT ANALYSISWITH CUSTOM PROGRAMS

JASON T. KILEY

UNIVERSITY OF GEORGIA

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Overview

Goal: high-level familiarization with data issues

where programming (Python 3.x) can help.

Retrieve the structure from semi-structured text.

Identify texts of interest within a larger set.

Output data in appropriate forms for content

analysis.

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Semi-structured text

Defined: Text that has a structure that cannot be

used directly.

Convert that semi-structure to a form we can use.

Examples: LexisNexis (press releases, news), web

pages, recent EDGAR documents.

Isolate the text we want to analyze, and use the

metadata.

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PRESS RELEASE 1

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PRESS RELEASE 1 & 2

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Regular expressions

Defined: a way of specifying search patterns.

Any given regex has a binary outcome dependingon whether it matches: true or false.

In some cases, this true or false result is enough.

We can also specify that we want to capture a part

of the matching text to use.

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Identifying interesting texts

Archival raw data is often full of texts that we are

not interested in.

Some data is naturally noisy.

Other times, over-gathering is easier if we can

automate the elimination of uninteresting texts.

Human labor is smart but expensive and slow;

computers are fast but only as smart as you can

make it.

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REGULAR EXPRESSIONS

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Output

Forms

Individual files: LIWC, comparisons.

Spreadsheets: coding, statistical analysis.

Testing and project management: gatherdescriptive statistics, diagnostics.

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OUTPUT: INDIVIDUAL FILES

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OUTPUT: SPREADSHEET

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Scratching the surface

If you can precisely describe how to do something,

even if that explanation is complex, a computer can

do the hard work.

Automate raw data gathering.

Use open source libraries for analysis.

Write your own algorithms to analyze content.

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COMMENTS ANDQUESTIONS