Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech

Abstract

Objective: The growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.

Design: Participants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.

Setting: Independent living sector of a senior housing community in San Diego County.

Participants: Eighty English-speaking older adults with age range 66-94 (mean 83 years).

Measurements: Interviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.

Results: Lonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).

Conclusions: AI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.

Keywords: Artificial Intelligence; gender; social isolation.

FIGURE 1

Processing pipeline for the qualitative interview data. API: application programming interface; NLU: natural language understanding; Q1: Question 1 (“Do you ever feel lonely, and if so, how often?”); Q2: Question 2 (“What does loneliness feel like to you? What is your general mood during that time?”); Q3: Question 3 (“Why do you think others may feel lonely?”); TF-IDF: term frequency – inverse document frequency.

FIGURE 2

Distribution of length of response to Question 1 by quantitatively assessed loneliness. Q1: Question 1 (“Do you ever feel lonely and if so, how often?”).

FIGURE 3

Emotional composition of response to Question 1 (“Do you ever feel lonely and if so, how often?”) by (A) Qualitative loneliness and (B) Quantitative assessment of loneliness (UCLA-3 Score). Dashed lines in the middle of distribution indicate median (second quartile) and dotted lines indicates first and third quartiles in the distribution.

FIGURE 4

Distribution of emotions (sadness, joy, fear, disgust, anger) in response to Question 1 (“Do you ever feel lonely and if so, how often?”) by sex. Dashed lines in the middle of distribution indicate median (second quartile) and dotted lines indicate first and third quartiles in the distribution.