1. SML2010: This dataset is collected from a monitor system mounted in a domotic house. It corresponds to approximately 40 days of monitoring data.
2. Online Retail II: A real online retail transaction data set of two years.
3. A study of Asian Religious and Biblical Texts: Mainly from Project Gutenberg, we combine Upanishads, Yoga Sutras, Buddha Sutras, Tao Te Ching and Book of Wisdom, Book of Proverbs, Book of Ecclesiastes and Book of Ecclesiasticus
4. YouTube Multiview Video Games Dataset: This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).
5. KDC-4007 dataset Collection: KDC-4007 dataset Collection is the Kurdish Documents Classification text used in categories regarding Kurdish Sorani news and articles.
6. Mturk User-Perceived Clusters over Images: This dataset was collected by Shan-Hung Wu and DataLab members at NTHU, Taiwan. There're 325 user-perceived clusters from 100 users and their corresponding descriptions.
7. Sports articles for objectivity analysis: 1000 sports articles were labeled using Amazon Mechanical Turk as objective or subjective. The raw texts, extracted features, and the URLs from which the articles were retrieved are provided.
8. Drug Review Dataset (Druglib.com): The dataset provides patient reviews on specific drugs along with related conditions. Reviews and ratings are grouped into reports on the three aspects benefits, side effects and overall comment.
9. Drug Review Dataset (Drugs.com): The dataset provides patient reviews on specific drugs along with related conditions and a 10 star patient rating reflecting overall patient satisfaction.
10. BuddyMove Data Set: User interest information extracted from user reviews published in holidayiq.com about various types of point of interests in South India
11. Travel Reviews: Reviews on destinations in 10 categories mentioned across East Asia. Each traveler rating is mapped as Excellent(4), Very Good(3), Average(2), Poor(1), and Terrible(0) and average rating is used.
12. Tarvel Review Ratings: Google reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.
13. CNAE-9: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a
subset of 9 categories
14. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
15. Real-time Election Results: Portugal 2019: Data set of the real-time election results of the 2019 Portuguese Parliamentary Election.
16. News Popularity in Multiple Social Media Platforms: Large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn.
17. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented.
18. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.
19. NIPS Conference Papers 1987-2015: This data set contains the distribution of words in the full text of the NIPS conference papers published from 1987 to 2015.
20. Miskolc IIS Hybrid IPS: The dataset was created for the comparison and evaluation of hybrid indoor positioning methods. The dataset presented contains data from W-LAN and Bluetooth interfaces, and Magnetometer.
21. TTC-3600: Benchmark dataset for Turkish text categorization: The TTC-3600 data set is a collection of Turkish news and articles including categorized 3,600 documents from 6 well-known portals in Turkey. It has 4 different forms in ARFF Weka format.
22. Bag of Words: This data set contains five text collections in the form of bags-of-words.
23. Opinion Corpus for Lebanese Arabic Reviews (OCLAR): Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services’ reviews, including hotels, restaurants, shops, and others.