Germeval18 - Text Classification Dataset

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Text Classification Dataset

Text Classification Dataset with Binary and Multi-class Labels

By Philipp Schmid (From Huggingface) [source]

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About this dataset

The dataset is provided in two separate files: train.csv and test.csv. The train.csv file contains a substantial amount of labeled data with columns for the text data itself, as well as their corresponding binary and multi-class labels. This enables users to develop and train machine learning models effectively using this dataset.

Similarly, test.csv includes additional examples for evaluating pre-trained models or assessing model performance after training on train.csv. It follows a similar structure as train.csv with columns representing text data, binary labels, and multi-class labels.

With its rich content and extensive labeling scheme for binary and multi-class classification tasks combined with its ease of use due to its tabular format in CSV files makes this dataset an excellent choice for anyone looking to advance their NLP capabilities through diverse text classification challenges

How to use the dataset

How to Use this Dataset for Text Classification

This guide will provide you with useful information on how to effectively utilize this dataset for your text classification projects.

Understanding the Columns

The dataset consists of several columns, each serving a specific purpose:

• text: This column contains the actual text data that needs to be classified. It is the primary feature for your modeling task.

• binary: This column represents the binary classification label associated with each text entry. The label indicates whether the text belongs to one class or another. For example, it could be used to classify emails as either spam or not spam.

• multi: This column represents the multi-class classification label associated with each text entry. The label indicates which class or category the text belongs to out of multiple possible classes. For instance, it can be used to categorize news articles into topics like sports, politics, entertainment, etc.

Dataset Files

The dataset is provided in two files: train.csv and test.csv.

• train.csv: This file contains a subset of labeled data specifically intended for training your models. It includes columns for both text data and their corresponding binary and multi-class labels.

• test.csv: In order to evaluate your trained models' performance on unseen data, this file provides additional examples similar in structure and format as train.csv. It includes columns for both texts and their respective binary and multi-class labels as well.

Getting Started

To make use of this dataset effectively, here are some steps you can follow:

• Download both train.csv and test.csv files containing labeled examples.
• Load these datasets into your preferred machine learning environment (such as Python with libraries like Pandas or Scikit-learn).
• Explore the dataset by examining its structure, summary statistics, and visualizations.
• Preprocess the text data as needed, which may include techniques like tokenization, removing stop words, stemming/lemmatizing, and encoding text into numerical representations (such as bag-of-words or TF-IDF vectors).
• Consider splitting the train.csv data further into training and validation sets for model development and evaluation.
• Select appropriate machine learning algorithms for your text classification task (e.g., Naive Bayes, Logistic Regression, Support Vector Machines) and train them

Research Ideas

• Sentiment Analysis: The dataset can be used to classify text data into positive or negative sentiment, based on the binary classification label. This can be helpful in analyzing customer reviews, social media sentiment, and feedback analysis.
• Topic Categorization: The multi-class classification label can be used to categorize text into different topics or themes. This can be useful in organizing large amounts of text data, such as news articles or research papers.
• Spam Detection: The binary classification label can be used to identify whether a text message or email is spam or not. This can help users filter out unwanted messages and improve their overall communication experience.
  Overall, this dataset provides an opportunity to create models for various applications of text classification such as sentiment analysis, topic categorization, and spam detection

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

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File: test.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Philipp Schmid (From Huggingface).