Trained models for multi-task multi-dataset learning for text classification in tweets
Citation:
Mishra, Shubhanshu (2019): Trained models for multi-task multi-dataset learning for text classification in tweets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1917934_V1
If you use this dataset, please cite it.
Related Code Shubhanshu Mishra. "SocialMediaIE". Github. 2019. https://github.com/socialmediaie/SocialMediaIE
Related Article Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
Related Presentation Mishra, S., Diesner J., Hands on advanced machine learning for information extraction from tweets tasks, data, and open source tools. Sep, 2019
Related Presentation Mishra, S., Rezapour, R. & Diesner, J. (2020). Social Media Information Extraction Tutorials
Dataset Description

Trained models for multi-task multi-dataset learning for text classification in tweets.
Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality.

Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py
See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details.

If you are using this data, please also cite the related article:
Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
Subject Social Sciences
Keywords twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning; sentiment; sarcasm; abusive content;
License CC BY
Corresponding Creator Shubhanshu Mishra
Downloaded 1073 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-1917934_V1 2019-09-17
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