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Text as Data

A New Framework for Machine Learning and the Social Sciences

Margaret E. Roberts, Brandon M. Stewart, Justin Grimmer, et al.

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Princeton University Press img Link Publisher

Sozialwissenschaften, Recht, Wirtschaft / Methoden der empirischen und qualitativen Sozialforschung

Beschreibung

A guide for using computational text analysis to learn about the social world

From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.

Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.

Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.


  • Overview of how to use text as data
  • Research design for a world of data deluge
  • Examples from across the social sciences and industry

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Margaret E. Roberts
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Schlagwörter

Principle, Patriotism, Vocabulary, Newspaper, Foreign worker, Engineering, Supervised learning, Cluster analysis, Far-left politics, Everyday life, Statistical model, Email client, Mixture model, Spectral method, Generative model, Absolute difference, Dimension, Quantitative research, Cross-validation (statistics), Coding (social sciences), Regularization (mathematics), Hand coding, Raw data, Philosophy, Topic model, Technological change, Politics, Conceptualization (information science), Content analysis, University of Illinois Press, Speech recognition, Political science, Inference, Neuroscience, Self-hatred, Observation, Confounding, Usability, Latent Dirichlet allocation, Criticism, Precision and recall, Measurement, Bayesian network, High- and low-level, Dictionary attack, Result, Burstiness, Prediction, Subject (philosophy), E. H. Carr, Embedding, Machine learning, Pronoun, Twitter, Tom Coburn, Qualitative research, Rare disease, N-gram, Sampling (statistics), Loss function, News conference, Author, Quantity, Theory, Covariate, Natural science, Nonverbal communication, Parameter (computer programming), Test set, Randomization, Unemployment, Word embedding, Concept, Computing, Tag cloud, Estimation, Grounded theory, Softmax function, Ontology (information science), Synonym, Data set, Accuracy and precision, Law firm, Naive Bayes classifier, Causal inference, Probability, Ideology, Social science