Automated Content Analysis

Valerie Hase

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Computational and Text Analysis Methods Digital Humanities and Scholarship Sentiment Analysis and Opinion Mining

Abstract

Abstract Due to the rise in processing power, advancements in machine learning, and the availability of large text corpora online, the use of computational methods including automated content analysis has rapidly increased. Automated content analysis is applied and developed across disciplines such as computer science, linguistics, political science, economics and – increasingly – communication science. This chapter offers a theoretical and applied introduction to the method, including promises and pitfalls associated with the method.

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References

1993Framing: Toward Clarification of a Fractured Paradigm 2009The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods 2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts 2014Structural Topic Models for Open‐Ended Survey Responses 2018Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology 1967The General Inquirer: A Computer Approach to Content Analysis.2017Computational Grounded Theory: A Methodological Framework 2009How to Analyze Political Attention with Minimal Assumptions and Costs

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2024Facebook and social representations of Filipino migrant life in Germany: a reflexive computational approach 2023Ethical challenges in contemporary quantitative content analysis 2023Identification of social scientifically relevant topics in an interview repository: a natural language processing experiment

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