Depression is brutal, it can range from feelings of sadness, loss, or anger that interfere with a person’s everyday activities to suicidal tendencies. In the past few months there have been multiple cases of famous people committing suicide because they were depressed and unable to cope with the feelings of isolation and stress brought about by the current pandemic. Unfortunately depression is not an easy thing to diagnose and there isn’t a single test to diagnose it. Doctors can diagnose it based on your symptoms and a psychological evaluation which in most cases includes questions about your:
- moods
- appetite
- sleep pattern
- activity level
- thoughts etc
But all of this requires a person to be open about their thoughts and that can be difficult at times due to the stigma associated with mental health issues. In all of the cases I was referring to earlier the common theme from the friends & acquaintances have been about how they wish they had known that xyz was depressed and if they had then maybe they could have helped.
The problem is that people don’t always come out and say that they are depressed and sometime the signals are very faint. So its very interesting to see the various efforts that are underway to identify these symptoms earlier and get the people the help they need faster so that they don’t have to face it alone. As part of this effort scientists at Canada’s University of Alberta have created a machine learning model that uses linguistic clues to indicate signs of depression in text communications like twitter messages and have published a paper on it (Augmenting Semantic Representation of Depressive Language: From Forums to Microblogs) in the ‘European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database’.
We discuss and analyze the process of creating word embedding feature representations specifically designed for a learning task when annotated data is scarce, like depressive language detection from Tweets. We start from rich word embedding pre-trained from a general dataset, then enhance it with embedding learned from a domain specific but relatively much smaller dataset. Our strengthened representation portrays better the domain of depression we are interested in as it combines the semantics learned from the specific domain and word coverage from the general language. We present a comparative analyses of our word embedding representations with a simple bag-of-words model, a well known sentiment lexicon, a psycholinguistic lexicon, and a general pre-trained word embedding, based on their efficacy in accurately identifying depressive Tweets. We show that our representations achieve a significantly better F1 score than the others when applied to a high quality dataset.
This is not the first study on the topic and it won’t be the last. The paper is fairly technical and from what I can understand they can identify potential signs of depression based on words used and phrasing. But am not sure how they are taking into account sarcasm and contextual clues. For example without the appropriate context things being said can be taken in many different ways and identifying the correct emotion behind the words can be tricky. When we interact in person or over phone things like body language or verbal cues give us additional context about how a person is feeling, unfortunately that is not the case with text and there is a huge potential for things to be taken out of context or in the wrong way. Another issue is how to differentiate between feelings of sadness and depression as the symptoms might be very similar.
We need human interactions, connections etc to address this issue and not another technology claiming to be a silver bullet as not everything can be solved by AI/ML and the low accuracy level on such solutions can only cause trouble down the line. Imagine such a system being implemented at workplaces, during interviews or on dating sites. If a system flagged you as a depressive then it could cost you your job, or your relationship.
What do you think?
– Suramya
