# INLS690-270 Sarcasm Detection — Data

来源：https://www.kaggle.com/competitions/2020fa-inls690-sarcasm-detection/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

<h2>File descriptions</h2>
<ul>
<li><strong>train.tsv</strong> - Your training data. It has one header line and 53,032 data lines. The first column is the category or label of each training example; the second column is a comment in Reddit; the third column is the parent post. You may or may not find the parent post useful in the classification.</li>
<li><strong>test.tsv</strong> - Your test data. It has one header line and 17,719 data lines. Same format as the training data, except that the first column is an ID instead of the class label of the line (which your algorithm is going to predict). The ID field in each line in your submission file should match that in the test data.</li>
<li><strong>sample-submission.csv</strong> - a sample submission file in the correct format. It only contains two columns, id and label. The id's should match the first column of test.tsv exactly, and the second column is your predicted class label (0 or 1) for the corresponding line in text data. Note that the two columns are separated by comma, and please include the exact headers "id,label" in your submission.</li>
</ul>
<h2>Data fields</h2>
<ul>
<li><strong>comment</strong> - [in both train.tsv and test.tsv] the Reddit comment (short text document). It is in free text format. </li>
<li><strong> parent_comment </strong> [in both train.tsv and test.tsv] the parent post that the comment replies to. It is in free text format. </li>
<li><strong>label</strong> - [only in train.tsv] the category of a training example. It takes values of either 0 or 1.</li>
<li><strong>id</strong> - [only in test.tsv] an Id unique to a test example. This number should match to the Id field of the submission file. It should be in the range of 0-17,718.</li>
</ul>