Inferring Latent User Properties from Texts Published in Social Media

TLDR

The study proposes a method to predict latent user attributes such as demographics, personality, emotions, and sentiments from Twitter texts. The authors use machine‑learning and NLP techniques to model individual tweets for emotions and aggregate them across a user’s tweets to infer traits like age, gender, income, education, relationship status, optimism, and life satisfaction, focusing on Ekman’s six basic emotions. The approach enables users to see how others perceive them and has applications in online sales, marketing, targeted advertising, large‑scale polling, and healthcare analytics.

Abstract

We demonstrate an approach to predict latent personal attributes including user demographics, online personality, emotions and sentiments from texts published on Twitter. We rely on machine learning and natural language processing techniques to learn models from user communications. We first examine individual tweets to detect emotions and opinions emanating from them, and then analyze all the tweets published by a user to infer latent traits of that individual. We consider various user properties including age, gender, income, education, relationship status, optimism and life satisfaction. We focus on Ekman’s six emotions: anger, joy, surprise, fear, disgust and sadness. Our work can help social network users to understand how others may perceive them based on how they communicate in social media, in addition to its evident applications in online sales and marketing, targeted advertising, large scale polling and healthcare analytics.