Introductory description

Psychologists are increasingly using large text and speech datasets to investigate the human mind. This trend raises two key questions: How can we effectively analyse such vast language data? And what unique insights can large language data offer into cognitive processes and human behaviour?

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

A 2-hour lecture and a 1-hour seminar each week.

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Best practices and ethical issues in analysing/collecting large data; Basic Programming; Corpus Analysis; Data; Visualisation; Text Mining; Sentiment Analysis; Network Analysis; Speech Analysis.

Learning outcomes

By the end of the module, students should be able to:

• Critically evaluate quantitative psychological research that uses large language datasets.
• Organise, process, visualise, and analyse large language datasets.
• Communicate, report, and interpret large language data science findings accurately and concisely.

Indicative reading list

Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39(3), 445–459. https://doi.org/10.3758/BF03193014

De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006. https://doi.org/10.3758/s13428-018-1115-7

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2021). Variability and consistency in early language learning: The Wordbank project. MIT Press.

Gablasova, D., Brezina, V., & McEnery, T. (2017). Collocations in corpus-based language learning research: Identifying, comparing, and interpreting the evidence. Language Learning, 67, 155–179. https://doi.org/10.1111/lang.12225

Gries, S. Th. (2009). Quantitative corpus linguistics with R: A practical introduction. Routledge.

Hills, T. T., Proto, E., Sgroi, D., & Seresinhe, C. I. (2019). Historical analysis of national subjective wellbeing using millions of digitized books. Nature Human Behaviour, 3(12), 1271–1275. https://doi.org/10.1038/s41562-019-0750-z

Wickham, H. (2010). ggplot2: Elegant graphics for data analysis. Springer.

Winter, B. (2020). Statistics for linguists: An introduction using R. Routledge.

Research element

Seminars will provide students with opportunities to practise new data science skills, present their projects, and attend data clinics for support with their reports and presentations.

Interdisciplinary

This module is inherently interdisciplinary, integrating psychology, data science, and computational linguistics to explore both psychological and linguistic phenomena.

Subject specific skills

Students will develop subject-specific skills in data management, programming, data visualisation, and quantitative analysis. These skills will enable them to analyse large datasets, critically evaluate data-driven research, and apply data science techniques to psychological research.

Transferable skills

Students will develop transferable skills in critical evaluation, data analysis, and communication. These skills will enable them to critically assess data-driven research, analyse and interpret large datasets, and report findings accurately and concisely.