Tweet Acts: A Speech Act Classifier for Twitter
This addresses the problem of understanding communication actions on social media for researchers or applications in natural language processing, though it is incremental as it builds on existing speech act classification methods.
The paper tackled speech act recognition on Twitter by creating a taxonomy of six speech acts and training a logistic regression classifier with semantic and syntactic features, achieving a state-of-the-art average F1 score of over 0.70.
Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than $0.70$. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.