Strategies in Computer-Assisted Text Analysis

Brier, Alan and De Giorgi, Elisabetta and Hopp, Bruno (2016) Strategies in Computer-Assisted Text Analysis. NCRM Working Paper. NCRM.

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Abstract

This paper reviews the logic of attempts to automate the processes involved in computer-assisted text analysis in the social sciences. Bayesian estimation methods in spatial analysis of variations in positions of political parties over time and Latent Dirichlet Allocation from the developing field of latent topic analysis are compared with the analysis of structures of word co-occurrences in the tradition of content analysis, using Procrustean individual differences scaling. Each depends in practice on concentrating attention on a limited number of word tokens regarded as meaningful while most are disregarded as inessential. By applying apparently competing strategies to the same set of party contributions to the 1997 budget debate in the Italian parliament, they can be shown to be complementary in character and should be applied as such in comparing material of this kind.

Item Type:Working Paper (NCRM Working Paper)
Subjects:1. Frameworks for Research and Research Designs > 1.22 Mixed Methods
4. Qualitative Data Handling and Data Analysis > 4.4 Content Analysis
4. Qualitative Data Handling and Data Analysis > 4.20 Textual Analysis
5. Quantitative Data Handling and Data Analysis > 5.10 Latent Variable Models
5. Quantitative Data Handling and Data Analysis > 5.12 Data Mining
5. Quantitative Data Handling and Data Analysis > 5.14 Non-Parametric Approaches
6. Mixed Methods Data Handling and Data Analysis > 6.3 Mixed Methods Approaches (other)
ID Code:3886
Deposited By: NCRM users
Deposited On:20 Jul 2016 15:37
Last Modified:27 Jul 2016 12:11

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