Stance classification of tweets using Skip Char Ngrams

Date: 11 April 2018

Venue: Annexe - Rodestraat 14 - 2000 Antwerpen

Time: 4:00 PM

Organization / co-organization: CLiPS

Short description: CLiPS colloquium with Yaakov HaCohen-Kerner

Stance classification of tweets using Skip Char Ngrams 

In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.

BIO - Yaakov HaCohen-Kerner is an associate professor in computer science at the Jerusalem College of Technology (JCT) - Machon Lev, Jerusalem, Israel. He graduated in statistics and computer science. His master degree and Ph.D. are in computer science. All these three degrees are from Bar-Ilan University, Ramat-Gan, Israel. His doctoral project has been prized both by The Information Processing Association of Israel and the "Ben-Gurion Fund for the Encouragement of Research". He developed the "Judge's Apprentice", a case-based decision-support system for judges (at bench-trials) for enhancing uniformity in sentencing. His research interests are: Torah and Science, Artificial Intelligence, Case-Based Processing, Intelligent Text Processing and Game Playing.