Dataset to Identify Behavioral Disorder

The purpose of the present data set is to detect positive or negative thoughts to identify behavioral disorders of individuals via a combination of Natural Language Processing (NLP) mechanisms. Negative thinking refers to a pattern of thinking negatively about yourself and your surroundings. While everyone experiences negative thoughts now and again, negative thinking seriously affects the way you feel about yourself and the world; it even interferes with work/study and everyday functioning. That could be a symptom of a mental illness, such as depression, anxiety, disorders, personality disorders, anger, sadness, frustration, guilt, embarrassment, irritation, jealousy, or fear and schizophrenia. Positive thinking is like an optimistic person who sees good things everywhere and is generally confident and hopeful of the future. From the optimist's point of view, the world is full of potential opportunities.

Individuals’ statuses were collected using Online Social Networks (OSN) like Facebook, Twitter, Instagram, and Reddit, matching both positive and negative thinking. For research purposes, thoughts were collected using specific positive keywords such as #Success, #Life, #Happiness, #Dream, etc. and negative keywords such as #Difficulties, #Feeling, #Failure, #Worry and many more. To find these keywords, as a reference, I have used a dataset [1] of 150 positive and 150 negative adjectives describing personality characters and another database [2] of English EMOtional TErms (EMOTE), which provides positive and negative subjective ratings for 1287 nouns and 985 adjectives.

References:
[1] Raslescu, Andreea; Kreicker, Sophie; Gillespie, Amy; Berners-Lee, William; Murphy, Susannah E; Harmer, Catherine J (2022), “UK dataset of valence, imageability and frequency ratings of 300 adjectives for use in cognitive-emotional tasks”, Mendeley Data, V1, doi: 10.17632/kgk3jbx9xb.1
[2] Grühn, D. (2016). An English Word Database of EMOtional TErms (EMOTE). Psychological Reports, 119(1), 290-308. https://doi.org/10.1177/0033294116658474