{
  "fetched_at": "2026-10-20T00:24:14Z",
  "source_url": "https://www.kaggle.com/competitions/f1-challenge/data",
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    {
      "name": "data-description",
      "content": "<p>Given Twitter US Airline Sentiment Dataset, which contains data for over 7500 tweets, your task is to predict the sentiment of the tweet i.e. positive, negative or neutral.</p>\n<p>You are given:\n<ol>\n<li> A Training dataset csv file with X train and Y train data </li>\n<li> A X test File and you have to predict and submit predictions for this file. </li>\n</ol>\n</p>\n<h2>Read Instructions carefully -</h2>\n<strong><p>\n<ol>\n<li> Files are in csv format. </li>\n<li> Submit a csv file with only predictions for X test data. File should not have any headers and should only have one column i.e. predictions. </li> \n<li> Submit your ipynb file as well. </li>\n<li> Your score is based on number of accurate predictions. </li>\n<li>  You need to upload both predictions and notebook file to submit.  </li>\n<li>  Please make sure that your submissions are genuine and you haven’t cheated/copied from anywhere.  <b> In such scenario your submission will be disqualified </b> </li>\n</ol>\n</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>Train.csv</strong> - the training set</li>\n<li><strong>Test.csv</strong> - the test set</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>tweet_id</strong> </li>\n<li><strong>airline</strong> </li>\n<li><strong>airline_sentiment</strong></li>\n<li><strong>airline_sentiment_gold</strong> </li>\n<li><strong>name</strong></li>\n<li><strong>negativereason_gold</strong></li>\n<li><strong>retweet_count</strong> </li>\n<li><strong>text</strong></li>\n<li><strong>tweet_coord</strong> </li>\n<li><strong>tweet_created</strong> </li>\n<li><strong>tweet_location</strong> </li>\n<li><strong>user_timezone</strong> </li>\n</ul>",
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    {
      "name": "Description",
      "content": "<p>All the best</p>\n<p>You have been provided with a  training dataset containing tweets of different users describing their flight experience with different airlines. Along with the tweets, different information have been provided.</p>\n<p>  The sentiment of the tweet has been mentioned in the 'airline_sentiment' column  of the dataset. The sentiment can have value - 'positive', 'negative'  and 'neutral'.</p>\n<p> For the test dataset, all the columns of the training dataset, except the 'airline_sentiment' column will be present.</p>\n<p> Your task is to predict the sentiment of the tweets present in the test dataset, as to whether it is 'positive', 'negative' or 'neutral'. </p>\n<p> Submit a 'predictions.csv' file containing the columns 'Id' and 'Predicted'. </p>\n",
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      "name": "Evaluation",
      "content": "<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: 'Id' and 'Predicted'. Predicted columns should have only three values - 'positive', 'negative' or 'neutral'. A different spelling will lead to incorrect answer.</p>\n<p> The predictions file should have header - 'Id' and 'Predicted'.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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