The Multilingual Reuters Collection dataset comprises over 11,000 articles from six classes in five languages, i.e., English (E), French (F), German (G), Italian (I), and Spanish (S). Source: Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network Image Source: https://papers.nips.cc/paper/2009/file/f79921bbae40a577928b76d2fc3edc2a-Paper.pdf

The DAWT dataset consists of Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. Source: DAWT: Densely Annotated Wikipedia Texts across multiple languages

The dataset contains the main components of the news articles published online by the newspaper named <a href="https://gazzettadimodena.gelocal.it/modena">Gazzetta di Modena</a>: url of the web page, title, sub-title, text, date of publication, crime category assigned to each news article by the author. The news articles are written in Italian and describe 11 types of crime events occurred in the province of Modena between the end of 2011 and 2021. Moreover, the dataset includes data derived from the abovementioned components thanks to the application of Natural Language Processing techniques. Some examples are the place of the crime event occurrence (municipality, area, address and GPS coordinates), the date of the occurrence, and the type of the crime events described in the news article obtained by an automatic categorization of the text. In the end, news articles describing the same crime events (duplciates) are detected by calculating the document similarity. Now, we are working on the application of question answering to extract the 5W+1H and we plan to extend the current dataset with the obtained data. Other researchers can employ the dataset to apply other algorithms of text categorization and duplicate detection and compare their results with the benchmark. The dataset can be useful for several scopes, e.g., geo-localization of the events, text summarization, crime analysis, crime prediction, community detection, topic modeling.

A general purpose text categorization dataset (NatCat) from three online resources: Wikipedia, Reddit, and Stack Exchange. These datasets consist of document-category pairs derived from manual curation that occurs naturally by their communities. Source: Natcat: Weakly Supervised Text Classification with Naturally Annotated Datasets

About the MNAD Dataset The MNAD corpus is a collection of over 1 million Moroccan news articles written in modern Arabic language. These news articles have been gathered from 11 prominent electronic news sources. The dataset is made available to the academic community for research purposes, such as data mining (clustering, classification, etc.), information retrieval (ranking, search, etc.), and other non-commercial activities. ## Dataset Fields - Title: The title of the article - Body: The body of the article - Category: The category of the article - Source: The Electronic News paper source of the article ## About Version 1 of the Dataset (MNAD.v1) Version 1 of the dataset comprises 418,563 articles classified into 19 categories. The data was collected from well-known electronic news sources, namely Akhbarona.ma, Hespress.ma, Hibapress.com, and Le360.com. The articles were stored in four separate CSV files, each corresponding to the news website source. Each CSV file contains three fields: Title, Body, and Category of the news article. The dataset is rich in Arabic vocabulary, with approximately 906,125 unique words. It has been utilized as a benchmark in the research paper: "A Moroccan News Articles Dataset (MNAD) For Arabic Text Categorization". In 2021 International Conference on Decision Aid Sciences and Application (DASA). This dataset is available for download from the following sources: - Kaggle Datasets : MNADv1 - Huggingface Datasets: MNADv1 ## About Version 2 of the Dataset (MNAD.v2) Version 2 of the MNAD dataset includes an additional 653,901 articles, bringing the total number of articles to over 1 million (1,069,489), classified into the same 19 categories as in version 1. The new documents were collected from seven additional prominent Moroccan news websites, namely al3omk.com, medi1news.com, alayam24.com, anfaspress.com, alyaoum24.com, barlamane.com, and SnrtNews.com. The newly collected articles have been merged with the articles from the previous version into a single CSV file named MNADv2.csv. This file includes an additional column called "Source" to indicate the source of each news article. Furthermore, MNAD.v2 incorporates improved pre-processing techniques and data cleaning methods. These enhancements involve removing duplicates, eliminating multiple spaces, discarding rows with NaN values, replacing new lines with "\n", excluding very long and very short articles, and removing non-Arabic articles. These additions and improvements aim to enhance the usability and value of the MNAD dataset for researchers and practitioners in the field of Arabic text analysis. This dataset is available for download from the following sources: - Kaggle Datasets : MNADv2 - Huggingface Datasets: MNADv2 ## Citation If you use our data, please cite the following paper: bibtex @inproceedings{MNAD2021, author = {Mourad Jbene and Smail Tigani and Rachid Saadane and Abdellah Chehri}, title = {A Moroccan News Articles Dataset ({MNAD}) For Arabic Text Categorization}, year = {2021}, publisher = {{IEEE}}, booktitle = {2021 International Conference on Decision Aid Sciences and Application ({DASA})} doi = {10.1109/dasa53625.2021.9682402}, url = {https://doi.org/10.1109/dasa53625.2021.9682402}, }

Este conjunto de datos consiste en comentarios de publicaciones del MINSA (Perú) en Facebook sobre la vacuna contra el VPH entre los años 2019 y 2020. Se leyó cuidadosamente cada uno de los comentarios, luego se procedió a clasificarlos de manera manual. Para esta clasificación se interpretó los mensajes de las personas, por lo que se analizó los hilos (comentarios y respuestas) por separado y se procedió a etiquetarlos por temas "Topic" . Un profesional de salud realizó una segunda clasificación y las discrepancias se resolvieron con un tercer profesional. Luego, se seleccionaron subcategorías que hacían referencia directa a las vacunas contra el VPH. La clasificación se realizó utilizando las siguientes categorías "topic_c" : - 0: El comentario tiene una postura contraria a la vacuna contra el VPH (antivacuna) - 1: El comentario tien una postura a favor de la vacuna contra el VPH (provacuna) - 2: El comentario refleja una duda o dudas relacionada con la vacuna contra el VPH - 3: El comentario habla de cualquier otra cosa Citar: - Lewis De La Cruz, Lucy Cordova, & Esperanza Reyes. (2023). <i>Text_VPH</i> [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/6460567