Detecting Cognitive Distortions from Patient-Therapist Interactions
An important part of Cognitive Behavioral Therapy (CBT) is to recognize and restructure certain negative thinking patterns that are also known as cognitive distortions. This project aims to detect these distortions using natural language processing. We compare and contrast different types of linguistic features as well as different classification algorithms and explore the limitations of applying these techniques on a small dataset.
Emotional Reasoning: Believing “I feel that way, so it must be true”
Overgeneralization: Drawing conclusions with limited and often un negative experience.
Mental Filter: Focusing only on limited negative aspects and not the excessive positive ones.
Should Statements: Expecting things or personal behavior should be a certain way.
All or Nothing: Binary thought pattern. Considering anything short of perfection as a failure.
Mind Reading: Concluding that others are reacting negatively to you, without any basis in fact.
Fortune Telling: Predicting that an event will always result in the worst possible outcome.
Magnification: Exaggerating or Catastrophizing the outcome of certain events or behavior.
Personalization: Holding oneself personally responsible for events beyond one’s control.
Labeling: Attaching labels to oneself or others (ex: “loser”, “perfect”).
In particular, this research aims to answer the following questions:
Which type of NLP features is more suitable for cognitive distortion detection: semantic or syntactic? Simply put, to compare what is said and how is it said in the context of this task. And, how important is word order in this context?
How well do these NLP features and ML classification algorithms perform this task with a limited-sized dataset?
the annotators were asked to determine a dominant distortion for each of the entries, and an optional secondary distortion if it is too hard to determine a dominant distortion
On the other hand, some findings are more unexpected. The expectation with the “should statement” was to have a higher probability of having auxiliary verbs such as ‘should’, ‘must’, ‘ought to’ etc. However, the results show that should statement have a lower than average probability of having auxiliary verbs. An example of this distortion without using any of the words listed above could be “While others my age are busy with their jobs and life I am just wasting my time”.