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  "fetched_at": "2026-10-24T17:24:59Z",
  "source_url": "https://www.kaggle.com/competitions/edsa-climate-change-belief-analysis/data",
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      "name": "data-description",
      "content": "Data\nThe collection of this data was funded by a Canada Foundation for Innovation JELF Grant to Chris Bauch, University of Waterloo.\nThe dataset aggregates tweets pertaining to climate change collected between Apr 27, 2015 and Feb 21, 2018. In total, 43943 tweets were collected. Each tweet is labelled as one of the following classes:\n\nClass \nDescription\n2\nNews: the tweet links to factual news about climate change\n1\nPro: the tweet supports the belief of man-made climate change\n0\nNeutral: the tweet neither supports nor refutes the belief of man-made climate change\n-1\nAnti: the tweet does not believe in man-made climate change\nVariable definitions\nColumns\nsentiment: Sentiment of tweet\nmessage: Tweet body\ntweetid: Twitter unique id\nFiles available for download\ntrain.csv - You will use this data to train your model.\ntest.csv - You will use this data to test your model.\nSampleSubmission.csv - is an example of what your submission file should look like. The order of the rows does not matter, but the names of the tweetid's must be correct.\n",
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      "name": "Description",
      "content": "Many companies are built around lessening one’s environmental impact or carbon footprint. They offer products and services that are environmentally friendly and sustainable, in line with their values and ideals. They would like to determine how people perceive climate change and whether or not they believe it is a real threat. This would add to their market research efforts in gauging how their product/service may be received.\n\nWith this context, EDSA is challenging you during the Classification Sprint with the task of  creating a Machine Learning model that is able to classify whether or not a person believes in climate change, based on their novel tweet data. \n\nProviding an accurate and robust solution to this task gives companies access to a broad base of consumer sentiment, spanning multiple demographic and geographic categories - thus increasing their insights and informing future marketing strategies.\n",
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      "name": "Evaluation",
      "content": "Weighted F1-score\n\nPrediction of an individual's climate change sentiment class \n\n| tweetid | sentiment |\n| --- | --- |\n|797132  |1  |\n|840869 |2|\n",
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      "name": "rules",
      "content": "This is a private hackathon whose primary purpose is for the members of the EXPLORE Data Science Academy to apply what they have learnt. If you are part of EDSA contact your mentor for the secret code.\n\nYou may use only the datasets provided within this competition. Your solution must use machine learning with an emphasis on classification analysis and related techniques.\n\nParticipation within this competition occurs on a team basis for full-time students, and on an individual basis for part-time students. Students are to consult their Predict instructions for further details around team-work. You must adhere to the rules associated with teaming up.\n\nMultiple accounts per user are not allowed. Collaboration across individuals not in the same team is not allowed, and collaboration between different teams is not allowed.\n\nCode must not be shared privately, except amongst individuals within the same team.\n\nThe solution must use publicly-available, open-source packages only. Maximum 20 solutions submitted per day. Your highest-scoring solution will be the one by which you are judged.\n",
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