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  • Primer
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Quantitative text analysis

Nature Reviews Methods Primers volume 4, Article number: 25 (2024) Cite this article

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Abstract

Text analysis has undergone substantial evolution since its inception, moving from manual qualitative assessments to sophisticated quantitative and computational methods. Beginning in the late twentieth century, a surge in the utilization of computational techniques reshaped the landscape of text analysis, catalysed by advances in computational power and database technologies. Researchers in various fields, from history to medicine, are now using quantitative methodologies, particularly machine learning, to extract insights from massive textual data sets. This transformation can be described in three discernible methodological stages: feature-based models, representation learning models and generative models. Although sequential, these stages are complementary, each addressing analytical challenges in the text analysis. The progression from feature-based models that require manual feature engineering to contemporary generative models, such as GPT-4 and Llama2, signifies a change in the workflow, scale and computational infrastructure of the quantitative text analysis. This Primer presents a detailed introduction of some of these developments, offering insights into the methods, principles and applications pertinent to researchers embarking on the quantitative text analysis, especially within the field of machine learning.

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Fig. 1: Schematic representation of three predominant approaches in the quantitative text analysis.
Fig. 2: Comparative workflows of model training (upper section) and model application or inference (lower section) for a text classification task in the context of the quantitative text analysis.

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Acknowledgements

K.L.N. was supported by grants from the Velux Foundation (grant title: FabulaNET) and the Carlsberg Foundation (grant number: CF23-1583). N.T. was supported by the research programme Change is Key! supported by Riksbankens Jubileumsfond (grant number: M21-0021).

Author information

Authors and Affiliations

  1. Center for Humanities Computing, Aarhus University, Aarhus, Denmark

    Kristoffer L. Nielbo

  2. Meertens Institute, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands

    Folgert Karsdorp

  3. Department of History, University of Amsterdam, Amsterdam, The Netherlands

    Melvin Wevers

  4. Institute of History, Leiden University, Leiden, The Netherlands

    Alie Lassche

  5. Department of Linguistics, Aarhus University, Aarhus, Denmark

    Rebekah B. Baglini

  6. Department of Literature, University of Antwerp, Antwerp, Belgium

    Mike Kestemont

  7. Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Gothenburg, Sweden

    Nina Tahmasebi

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  1. Kristoffer L. Nielbo
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  2. Folgert Karsdorp
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  3. Melvin Wevers
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  4. Alie Lassche
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  5. Rebekah B. Baglini
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Contributions

Introduction (K.L.N. and F.K.); Experimentation (K.L.N., F.K., M.K. and R.B.B.); Results (F.K., M.K., R.B.B. and N.T.); Applications (K.L.N., M.W. and A.L.); Reproducibility and data deposition (K.L.N. and A.L.); Limitations and optimizations (M.W. and N.T.); Outlook (M.W. and N.T.); overview of the Primer (K.L.N.).

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Correspondence to Kristoffer L. Nielbo.

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Nature Reviews Methods Primers thanks F. Jannidis, L. Nelson, T. Tangherlini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Application programming interface

A set of rules, protocols and tools for building software and applications, which programs can query to obtain data.

Bag-of-words model

A model that represents text as a numerical vector based on word frequency or presence. Each text corresponds to a predefined vocabulary dictionary, with the vector.

Computer linguistics

Intersection of linguistics, computer science and artificial intelligence that is concerned with computational aspects of human language. It involves the development of algorithms and models that enable computers to understand, interpret and generate human language.

Corpus linguistics

The branch of linguistics that studies language as expressed in corpora (samples of real-world text) and uses computational methods to analyse large collections of textual data.

Data augmentation

A technique used to increase the size and diversity of language data sets to train machine-learning models.

Data science

The application of statistical, analytical and computational techniques to extract insights and knowledge from data.

Fleiss’ kappa

(κ). A statistical measure used to assess the reliability of agreement between multiple raters when assigning categorical ratings to a number of items.

Frequency bias

A phenomenon in which elements that are over-represented in a data set receive disproportionate attention or influence in the analysis.

Information retrieval

A field of study focused on the science of searching for information within documents and retrieving relevant documents from large databases.

Lemmatization

A text normalization technique used in natural language processing in which words are reduced to their base or dictionary form.

Machine learning

In quantitative text analysis, machine learning refers to the application of algorithms and statistical models to enable computers to identify patterns, trends and relationships in textual data without being explicitly programmed. It involves training these models on large data sets to learn and infer from the structure and nuances of language.

Natural language processing

A field of artificial intelligence using computational methods for analysing and generating natural language and speech.

Recommender system

A type of information filtering system that seeks to predict user preferences and recommend items (such as books, movies and products) that are likely to be of interest to the user.

Representation learning

A set of techniques in machine learning in which the system learns to automatically identify and extract useful features or representations from raw data.

Stemming

A text normalization technique used in natural language processing, in which words are reduced to their base or root form.

Supervised learning

A machine-learning approach in which models are trained on labelled data, such that each training text is paired with an output label. The model learns to predict the output from the input data, with the aim of generalizing the training set to unseen data.

Transformer

A deep learning model that handles sequential data, such as text, using mechanisms called attention and self-attention, allowing it to weigh the importance of different parts of the input data. In the quantitative text analysis, transformers are used for tasks such as sentiment analysis, text classification and language translation, offering superior performance in understanding context and nuances in large data sets.

Unsupervised learning

A type of machine learning in which models are trained on data without output labels. The goal is to discover underlying patterns, groupings or structures within the data, often through clustering or dimensionality reduction techniques.

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Nielbo, K.L., Karsdorp, F., Wevers, M. et al. Quantitative text analysis. Nat Rev Methods Primers 4, 25 (2024). https://doi.org/10.1038/s43586-024-00302-w

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