Task 1 emulates previous humor detection tasks in which all ratings were averaged to provide mean classification and rating scores.
Task 2 aims to predict how offensive a text would be (for an average user) with values between 0 and 5.
J. A. Meaney, University of Edinburgh, firstname.lastname@example.org
Steven Wilson, University of Edinburgh, email@example.com
Luis Chiruzzo, Universidad de la Republica, firstname.lastname@example.org
Walid Magdy , University of Edinburgh, email@example.com
Sentiment analysis is one of the most useful natural language processing applications. There are many papers and systems addressing this task, but most of the work is focused on English. Therefore, we present Mazajak, an online system for Arabic sentiment analysis. The system is based on a deep learning model, which achieves state-of-the-art results on many Arabic dialect datasets including SemEval 2017 and ASTD. The system provides three-way sentiment classification to one of the classes (Positive, Negative, Neutral)
Mazajak provides many features such as sentiment analysis for a sentence, a file, or you can submit a Twitter account and get an analysis of the user. In addtion to that there is an online API.
Mazajak was created by Ibrahim Abu Farha and Dr. Walid Magdy at the ILCC, part of the School of Informatics, the Univeristy of Edinburgh.
This project was funded by The Alan Turing Institute, UK. The details about the system were published in WANLP-2019, please cite the following paper:
Mazajak: An Online Arabic Sentiment Analyser. Ibrahim Abu Farha and Walid Magdy. In Proceedings of the Fourth Arabic Natural Language Processing Workshop (WANLP). 2019.
You can access the online tool from here.
This resource contains a set of English-language word embeddings trained on the entirety of Urban Dictionary (urbandictionary.com) as of October 16, 2019. All terms, definitions, examples, and tags were treated as running text and embeddings were learned using the fasttext framework (fasttext.cc) with window size of 5, a negative sampling rate of 10, and a word-level dimensionality of 300.
These embeddings perform competitively on a range on word-level semantics tasks, and were also useful initializations for classifiers trained for sentiment and sarcasm detection. If you are working in a domain that uses a high degree of slang or nonstandard English and you want representations that better capture the slang meanings of terms, give them a try!
If you use this resource in your work, please cite: Wilson, S. R., Magdy, W., McGillivray, B., Garimella, K., & Tyson, G. Urban Dictionary Embeddings for Slang NLP Applications. In Proceedings of the Language Resources and Evaluation Conference (LREC). Marseille, France, May 2020
You can download the file from here.
Stance detection involves the identification of the positions of a piece of text or a user towards a target such as a topic, entity, or claim. A growing body of research in the ICWSM and Social Computing community on performing and using stance detection shows its importance for a variety of applications including properly analyzing the attitudes of online users.
This tutorial aims to teach participants how to perform and use stance detection. Specifically, we provide a general introduction to the concept of stance and how it differs from sentiment analysis; present recent methodologies for stance detection on social media including supervised, semi-supervised, and unsupervised methods; and introduce various applications of stance detection on social media including how it can be used to support analytical studies. The tutorial concludes with an exploration of open challenges and future directions for stance detection on social media.
Abeer AlDayel PhD student at the school of Informatics, the University of Edinburgh. Her work is on stanc detection on social media. Website: https://abeeraldayel.github.io/
Kareem Darwish Principle scientist at Qatar Computing Research Institute, HBKU university. Website: http://kareemdarwish.com/
Walid Magdy Associate professor at the School of Informatics, the University of Edinburgh, and faculty fellow at the Alan Turing Institute. Website: http://homepages.inf.ed.ac.uk/wmagdy/