Depression Corpus of Arabic tweets
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This is a new Arabic corpus for depression detection from Twitter. This corpus consists of 10.000 tweets classified into 5.000 of 1 which means 'depressed' tweets, and 5.000 of 0 which means 'non-depressed' tweets. Additionally, the fetched tweets covered a wide range of different synonymous of depression and cheerfulness on Twitter. To our knowledge, this is the first manually annotated Arabic corpus for depression detection from Twitter up to submitting this data' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["depression detection, depressed, anxiety, suicidal"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This is a new Arabic corpus for depression detection from Twitter. This corpus consists of 10.000 tweets classified into 5.000 of 1 which means 'depressed' tweets, and 5.000 of 0 which means 'non-depressed' tweets. Additionally, the fetched tweets covered a wide range of different synonymous of depression and cheerfulness on Twitter. To our knowledge, this is the first manually annotated Arabic corpus for depression detection from Twitter up to submitting this data' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This is a new Arabic corpus for depression detection from Twitter. This corpus consists of 10.000 tweets classified into 5.000 of 1 which means 'depressed' tweets, and 5.000 of 0 which means 'non-depressed' tweets. Additionally, the fetched tweets covered a wide range of different synonymous of depression and cheerfulness on Twitter. To our knowledge, this is the first manually annotated Arabic corpus for depression detection from Twitter up to submitting this data
depression detection, depressed, anxiety, suicidal