This is text document classification dataset which contains 2225 text data and five categories of documents. Five categories are politics, sport, tech, entertainment and business. We can use this dataset for documents classification and document clustering.

About Dataset - Dataset contains two features text and label. - No. of Rows : 2225 - No. of Columns : 2

Text: It contains different categories of text data Label: It contains labels for five different categories : 0,1,2,3,4

1. Politics = 0
2. Sport = 1
3. Technology = 2
4. Entertainment =3
5. Business = 4

This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website.

The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.

The dataset has the following features :

Data Set Characteristics: Multivariate

Number of Instances: 50425

Number of classes: 4

Area: Computer science

Attribute Characteristics: Real

Number of Attributes: 1

Associated Tasks: Classification

Missing Values? No

Gautam. (2019). E commerce text dataset (version - 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3355823

This dataset consists of an approx 50k collection of research articles from PubMed repository. Originally these documents are manually annotated by Biomedical Experts with their MeSH labels and each article are described in terms of 10-15 MeSH labels. In this Dataset we have huge numbers of labels present as a MeSH major, raising the issue of extremely large output space and severe label sparsity issues. To solve this issue, the Dataset has been Processed and mapped to its root as described below. https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture111.PNG" alt="Mapped Image not Fetched"> https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture22.PNG" alt="Tree Structure">

Context

This dataset contains Australian legal cases from the Federal Court of Australia (FCA). The cases were downloaded from AustLII. All cases from the year 2006,2007,2008 and 2009 are included. For each document , catchphrases, citations sentences, citation catchphrases, and citation classes are captured. Citation classes are indicated in the document, and indicate the type of treatment given to the cases cited by the present case.

Exploration Ideas

• Create a model to perform text classification on legal data
• EDA to identify top keywords related to every type of case category

Acknowledgements

Credits: Filippo Galgani galganif '@' cse.unsw.edu.au School of Computer Science and Engineering The Univeristy of New South Wales, Australia

The Facebook Text Classification Dataset consists of 5,000 social media posts designed for text analytics and machine learning applications. Each entry represents a Facebook post enriched with attributes such as post content, timestamp, language, engagement metrics, and labels for category, sentiment, and spam detection. The dataset covers ten diverse categories, including personal updates, news, events, promotions, memes, sports, politics, and health-related content, making it suitable for multi-class classification tasks. Sentiment labels (positive, neutral, negative) enable sentiment analysis, while the is_spam field supports spam detection models. Engagement features such as likes, comments, and shares allow exploration of user interaction patterns and predictive modeling of content popularity. With multilingual posts in English, Hindi, Spanish, French, and German, the dataset is ideal for NLP research, including topic classification, polarity detection, engagement forecasting, and multilingual processing, making it a versatile resource for social media analytics.

Dataset

This dataset was created by IceTea

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Shwet Prakash

Released under CC0: Public Domain

Contents

If you use this dataset, please cite it as follows: Paul, A., Mittal, O., Ghosh, S., Dasgupta, S., Bhattacharjee, D., Sarkar, R. (2024). COMSYS Hackathon-1 2023: Igniting Machine Learning Marvels. In: Kole, D.K., Roy Chowdhury, S., Basu, S., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of 4th International Conference on Frontiers in Computing and Systems. COMSYS 2023. Lecture Notes in Networks and Systems, vol 974. Springer, Singapore. https://doi.org/10.1007/978-981-97-2611-0_29

this is the csv and clean version of this dataset link_to_the_original_Data. You can use this data to train your NLP skills.

Dataset

This dataset was created by Subba Reddy Jinugu

Contents

Context

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. More information, can be found using this link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

Content

These datasets consist of news article headlines. These headlines are labelled as either 0, 1, 2 and 3, these values correspond to 4 types of news topics which are 'World', 'Sports', 'Business' and 'Sci/Tech'.

Acknowledgements

I installed the AG's news topic classification training dataset which is available from the huggingface datasets library. The AG's news topic classification training dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the AG's corpus of news articles. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

Dataset

This dataset was created by Fabio Fontana

Contents

Email Spam Classification, Text Classification Dataset

The dataset consists of a collection of emails categorized into two major classes: spam and not spam. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

The spam emails in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.

The non-spam emails in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.

The dataset encompasses emails of varying lengths, languages, and writing styles, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.

The dataset's possible applications:

• spam detection
• fraud detection
• email filtering systems
• customer support automation
• natural language processing

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F4d1fdedb2827152696dd0c0af05fd8da%2Ff.png?generation=1690286497115141&alt=media" alt="">

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

File with the extension .csv

includes the following information:

• title: title of the email,
• text: text of the email,
• type: type of the email

Email spam might be collected in accordance with your requirements.

keywords: spam mails dataset, email spam classification, spam or not-spam, spam e-mail database, spam detection system, email spamming data set, spam filtering system, spambase, feature extraction, spam ham email dataset, classifier, machine learning algorithms, cybersecurity, text dataset, sentiment analysis, llm dataset, language modeling, large language models, text classification, text mining dataset, natural language texts, nlp, nlp open-source dataset, text data

Dataset

This dataset was created by 𝔄ℌ𝔐𝔈𝔇 𝔄𝔖ℌℜ𝔄𝔉

Contents

Dataset

This dataset was created by Anna Jazayeri

Released under MIT

Contents

Dataset

This dataset was created by Marjia Ahmed

Contents

(WELFake) is a dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, authors merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training.

Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real).

There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame.

Published in: IEEE Transactions on Computational Social Systems: pp. 1-13 (doi: 10.1109/TCSS.2021.3068519).

Dataset

This dataset was created by YAZAN ALSHUAIBI

Contents

This dataset was built with a web scraping tool for the Dataton 2022 of Bancolombia for training supervised models to use in a News recommendation of the following categories:

1. Macroeconomics
2. Sustainability
3. Innovation
4. Regulations
5. Alliances
6. Reputation
7. Other

Columns

This CSV document consists of the following columns:

1. Url: source of the information, but it could be unavailable in some months
2. News: text of the news - used for classification
3. Type: Label of type of news.

This dataset contains text samples labeled as either human-written or AI-generated. It is designed for binary text classification tasks in Natural Language Processing (NLP). The dataset includes 1299 text samples with accompanying basic features such as word count, character count, average word length, and punctuation density.

The AI-generated texts were collected from multiple LLMs (e.g., ChatGPT, Gemini, Claude). Exact model attribution for each sample is not preserved.