Using personal writings to detect dementia: A text mining approach

Abstract

A novel text mining pilot for dementia detection using Linguistic Inquiry and Word Count (LIWC) was tested on public figures’ writings looking at word choice and affect compared to those with and without dementia. The differences found in this analysis mirror the expected patterns where writings of people with dementia reflect significantly more analytical thinking words, but significantly less authentic and emotional tone. In addition, the analysis found that people with dementia use significantly less functional words, such as grammar, and affections (happiness, sadness, anger, sadness), but tend to use significantly more pronouns in their writings. Written samples of those with dementia also use significantly less time-oriented words that indicate past, present, or future. The analysis of free form text suggests a potential avenue for detecting early changes that correlate with dementia, allowing for early preventative treatment before noticeable cognitive impairment occurs.