{
  "fetched_at": "2026-10-20T07:45:12Z",
  "source_url": "https://www.kaggle.com/competitions/comment-crush/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The train.csv contains the comment id, comment and a column positive, which highlights whether the statement is positive (1) or not (0).\ntest.csv follows the same structure.\n\n## Files\n\n*   **Dataset_Kaggle.csv** - the training set\n*   **Test_upload.csv** - the test set\n*   **Sample Submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `Id` - comment id.\n*   `Comment` - The comment to be analysed.\n*   `Positive` - The comment is positive or not.",
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    {
      "name": "Description",
      "content": "Are you trying to begin with NLP? \nThis may be the proper competition for you, or maybe not 🤣.\nThe competition aims to classify whether or not the sentiment associated with a particular comment is positive. It is of instrumental importance to the service industry. With the advent of emojis, it has become even more challenging to identify the correct meaning of a comment via ML models.\nIf you want to test your robustness to classify the comments irrespective of sarcasm and build a model to decipher the meaning of what a review intends to say, dive straight into the competition.\nSo, no time to dilly dally (iykyk). Get Started.\n## Acknowledgements\nThe dataset is an amalgamation of sentiment-labeled sentences datasets from UCI and additions by Jha Rohan and PV Sekhar.",
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    {
      "name": "Evaluation",
      "content": "The evaluation criterion is categorization accuracy. Upload the submission file in the format as described in the Data section.",
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    {
      "name": "rules",
      "content": "- Do not cheat. Try to come up with novel methods and try many things.\n- Refer to any material on the Earth, but do not try to dig up the dataset, you will not find it.\n- Enjoy!!",
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}