This paper describes the University of Sheffield's submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is to classify the stance of a tweet towards a target as "favor", "against", or "none". In Subtask B, the targets in the test data are different from the targets in the training data, thus rendering the task more challenging but also more realistic. To address the lack of target-specific training data, we use a large set of unlabelled tweets containing all targets and train a bag-of-words autoencoder to learn how to produce feature representations of tweets. These feature representations are then used to train a logistic regression classifier on labelled tweets, with additional features such as an indicator of whether the target is contained in the tweet. Our submitted run on the test data achieved an F1 of 0.3270.
A submission REF to the SemEval 2016 Twitter Stance Detection task focuses on creating a bag-of-words auto encoder, and training it over the tokenized tweets.
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
SemEval@NAACL-HLT
Computer Science