1. CLINC150: This is a intent classification (text classification) dataset with 150 in-domain intent classes. The main purpose of this dataset is to evaluate various classifiers on out-of-domain performance.
2. Sentiment Labelled Sentences: The dataset contains sentences labelled with positive or negative sentiment.
3. Multimodal Damage Identification for Humanitarian Computing: 5879 captioned images (image and text) from social media related to damage during natural disasters/wars, and belong to 6 classes: Fires, Floods, Natural landscape, Infrastructural, Human, Non-damage.
4. SMS Spam Collection: The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research.
5. Twitter Data set for Arabic Sentiment Analysis: This problem of Sentiment Analysis (SA) has been studied well on the English language but not Arabic one. Two main approaches have been devised: corpus-based and lexicon-based.
6. Youtube cookery channels viewers comments in Hinglish: The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel.
The data set is in Hinglish Language.
7. University of Tehran Question Dataset 2016 (UTQD.2016): Persian questions gathered from a jeopardy game broadcasted on Iranian national television.
8. YouTube Comedy Slam Preference Data: This dataset provides user vote data on which video from a pair of videos is funnier collected on YouTube Comedy Slam. The task is to automatically predict this preference based on video metadata.
9. YouTube Spam Collection: It is a public set of comments collected for spam research. It has five datasets composed by 1,956 real messages extracted from five videos that were among the 10 most viewed on the collection period.
10. Reuters-21578 Text Categorization Collection: This is a collection of documents that appeared on Reuters newswire in 1987. The documents were assembled and indexed with categories.
11. microblogPCU: MicroblogPCU data is crawled from sina weibo microblog[http://weibo.com/]. This data can be used to study machine learning methods as well as do some social network research.
12. Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network: One of the primary challenges in identifying the risks of the Burst Header Packet (BHP) flood attacks in Optical Burst Switching networks (OBS) is the scarcity of reliable historical data.
13. Detect Malware Types: Provide a short description of your data set (less than 200 characters).
14. Victorian Era Authorship Attribution: To create the largest authorship attribution dataset, we extracted works of 50 well-known authors. To have a non-exhaustive learning, in training there are 45 authors whereas, in the testing, it's 50
15. DeliciousMIL: A Data Set for Multi-Label Multi-Instance Learning with Instance Labels: This dataset includes 1) 12234 documents (8251 training, 3983 test) extracted from DeliciousT140 dataset, 2) class labels for all documents, 3) labels for a subset of sentences of the test documents.
16. Amazon Commerce reviews set: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
17. Farm Ads: This data was collected from text ads found on twelve websites that deal with various farm animal related topics. The binary labels are based on whether or not the content owner approves of the ad.