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A User-friendly and Powerful R Analysis of Large-scale Datasets

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A computational approach to qualitative analysis in large textual datasets.

Michael S Evans¹

¹Neukom Institute for Computational Science and Department of Film & Media Studies, Dartmouth College, Hanover, New Hampshire, United States of America.

|February 6, 2014

Summary

This study introduces computational techniques, like probabilistic topic modeling, to analyze large text datasets. These methods enhance qualitative analysis by identifying significant discussion subjects in public discourse.

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Area of Science:

Computational Social Science
Digital Humanities
Textual Analysis

Background:

Traditional qualitative analysis faces limitations with large datasets.
Measuring impact and validating inferences in discourse studies is challenging.

Purpose of the Study:

To introduce computational techniques for analyzing large textual datasets.
To demonstrate the application of probabilistic topic modeling in public discourse analysis.
To overcome methodological limitations of conventional qualitative research.

Main Methods:

Probabilistic topic modeling was employed.
Analysis of 14,952 newspaper documents from 1980-2012.
Computational data mining techniques were utilized.

Main Results:

Identified and evaluated qualitatively distinct subjects of discussion.
Demonstrated the significance of computational methods in analyzing public discourse.
Showcased how to measure case impact and validate inferences from large textual data.

Conclusions:

Computational techniques extend qualitative analysis capabilities for large datasets.
These methods offer solutions to methodological challenges in discourse studies.
Probabilistic topic modeling is effective for identifying patterns in public discourse.