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"""Depression: Reddit Dataset (Cleaned)""" |
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import csv |
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import json |
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import os |
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import datasets |
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from datasets.tasks import TextClassification |
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_DESCRIPTION = """\ |
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The dataset provided is a Depression: Reddit Dataset (Cleaned)containing approximately |
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7,000 labeled instances. It consists of two main features: 'clean_text' and 'is_depression'. |
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The 'clean_text' feature contains the text data from Reddit posts related to depression, while |
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the 'is_depression' feature indicates whether a post is classified as depression or not. |
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The raw data for this dataset was collected by web scraping Subreddits. To ensure the data's |
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quality and usefulness, multiple natural language processing (NLP) techniques were applied |
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to clean the data. The dataset exclusively consists of English-language posts, and its |
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primary purpose is to facilitate mental health classification tasks. |
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This dataset can be employed in various natural language processing tasks related to |
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depression,such as sentiment analysis, topic modeling, text classification, or any other NLP |
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task that requires labeled data pertaining to depression from Reddit. |
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""" |
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_TRAIN_URL = "depression_reddit_cleaned_ds.csv" |
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class DepressionRedditCleaned(datasets.GeneratorBasedBuilder): |
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""" |
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~7000 Cleaned Reddit Labelled Dataset on Depression |
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The raw data is collected through web-scrapping Subreddits and is cleaned using multiple NLP techniques. |
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The data is only in English. It mainly targets mental health classification. |
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""" |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"clean_text": datasets.Value("string"), |
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"is_depression": datasets.features.ClassLabel( |
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num_classes=2, |
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names=["not_depression", "depression"] |
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) |
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} |
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), |
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task_templates=[TextClassification(text_column="clean_text", label_column="is_depression")] |
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) |
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def _split_generators(self, dl_manager): |
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train_path = dl_manager.download_and_extract(_TRAIN_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": train_path} |
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) |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples as (key, example) tuples.""" |
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with open(filepath, encoding="utf-8") as f: |
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csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True) |
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next(csv_reader) |
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for id_, row in enumerate(csv_reader): |
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clean_text, is_depression = row |
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yield id_, {"clean_text": clean_text, "is_depression": is_depression} |
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