LonelyText: A Short Messaging Based Classification of Loneliness

TL;DR

This paper introduces LonelyText, a method that analyzes language patterns in instant messaging to predict loneliness, offering new insights into the linguistic markers associated with this emotional state.

Contribution

The paper presents a novel approach to predicting loneliness through short messaging analysis, providing promising directions for future research in text-based loneliness detection.

Findings

Language patterns correlate with loneliness.

Text mining from instant messaging can predict loneliness.

Insights offer new directions for emotional state detection.

Abstract

Loneliness does not only have emotional implications on a person but also on his/her well-being. The study of loneliness has been challenging and largely inconclusive in findings because of the several factors that might correlate to the phenomenon. We present one approach to predicting this event by discovering patterns of language associated with loneliness. Our results show insights and promising directions for mining text from instant messaging to predict loneliness.