Understanding autobiographical memory content using computational text analysis
Published in Memory, 2022
Recommended citation: Yeung, R. C., Stastna, M., & Fernandes, M. A. (2022). Understanding autobiographical memory content using computational text analysis. Memory, 30(10), 1267–1287. https://doi.org/10.1080/09658211.2022.2104317
Although there is scholarly interest in autobiographical memory (AM) content, past manual approaches are prohibitively time- and labour-intensive. Using structural topic modelling, we identified coherent topics (e.g., “Negative past relationships”, “Conversations”, “Experiences with family members”) within recurrent IAMs and found that topic use significantly differed depending on the valence of these memories. Computational methods allowed us to analyze AM content at an unprecedented scope and scale.
Recommended citation: Yeung, R. C., Stastna, M., & Fernandes, M. A. (2022). Understanding autobiographical memory content using computational text analysis. Memory, 30(10), 1267–1287. https://doi.org/10.1080/09658211.2022.2104317