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Binary and Multi-Class Text Classifier Data
Data Science and Analytics
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About
This dataset is designed for text classification tasks, providing a robust collection of labelled data ideal for developing and training machine learning models. It offers significant utility for advancing natural language processing capabilities, enabling a wide array of text classification challenges. The dataset facilitates the creation of models for applications such as sentiment analysis, topic categorisation, and the detection of unwanted messages.
Columns
The dataset is organised into two main files:
train.csv and test.csv. Both files share an identical tabular structure with the following columns:- text: This column contains the raw textual data that serves as the primary feature for classification.
- binary: This column provides a binary classification label for each text entry, indicating membership in one of two distinct classes (e.g., positive/negative sentiment, spam/not spam).
- multi: This column offers a multi-class classification label for each text entry, assigning it to one of several possible categories or themes (e.g., sports, politics, entertainment).
Distribution
The dataset is supplied in a tabular CSV format, consisting of two separate files:
train.csv and test.csv. The train.csv file contains a substantial amount of labelled data. Specific row or record counts for these files are not available within the provided sources.Usage
This dataset is well-suited for a variety of applications and use cases:
- Sentiment Analysis: Classifying text data into positive or negative sentiment, useful for analysing customer reviews, social media sentiment, and feedback.
- Topic Categorisation: Categorising text into different topics or themes, aiding in the organisation of large volumes of text data like news articles or research papers.
- Spam Detection: Identifying unwanted messages or emails, thereby helping users to filter out undesirable communications.
- Developing and training diverse machine learning models for text classification projects.
- Evaluating the performance of pre-trained models or assessing model efficacy after initial training.
Coverage
The dataset's regional scope is global. It was listed on 26/06/2025. Information regarding specific time ranges for the data content itself or any demographic scope is not detailed in the available sources.
License
CC0
Who Can Use It
This dataset is intended for:
- Data Scientists and Machine Learning Engineers aiming to develop and refine text classification models.
- Individuals seeking to enhance their Natural Language Processing (NLP) skills through practical text classification challenges.
- Researchers interested in sentiment analysis, topic modelling, or spam detection applications.
- Anyone requiring labelled text data for training, validation, or evaluation of machine learning algorithms.
Dataset Name Suggestions
- Germeval18 Text Classification Dataset
- Multipurpose Text Classification Data
- NLP Classification Dataset
- Labeled Text Data for ML
- Binary and Multi-Class Text Classifier Data
Attributes
Original Data Source: Germeval18 - Text Classification Dataset
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