There may be times when you may have said, “Haha, that’s so sad. Alexa, play Despacito,” but ironically Artificial Intelligence can now detect if you’re really sad or joking.
According to a recently published paper by researchers at UC San Diego School of Medicine used AI to analyze language patterns of adults to discern loneliness. The paper has been published in the American Journal of Geriatric Psychiatry. It attempts to assess the breadth and depth of loneliness through voice.
Author Dr. Ellen Lee, assistant professor of psychiatry at UCSD School of Medicine told Patch “Most studies use either a direct question of “How often do you feel lonely?” which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale, which does not explicitly use the word lonely.”
The study has used natural language processing or NLP which is an unbiased quantitative assessment of expressed emotion with the usual loneliness measurement tools.
NLP encompasses a variety of processes and techniques that analyze large volumes of unstructured speech and text data. In recent years, the advancement of AI and machine learning systems have bought to light, several preliminary studies to suggest conditions such as PTSD, psychosis, bipolar disorder, and depression, all of which might be detected just by analyzing natural speech.
Varsha Badal, a postdoctoral research fellow and author on the report, said “NLP and machine learning allow us to systematically examine long interviews from many individuals and explore how subtle speech features like emotions may indicate loneliness. Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize.”
Reportedly, the AI system can predict the loneliness of a person with 94% accuracy.
The researchers concluded in the study “Eventually, complex AI systems could intervene in real-time to help individuals to reduce their loneliness by adopting in positive cognitions, managing social anxiety, and engaging in meaningful social activities.”