What Are Tweeters Doing: Recognizing Speech Acts in Twitter

Speech acts provide good insights into the communicative behavior of tweeters on Twitter. This paper is mainly concerned with speech act recognition in Twitter as a multi-class classification problem, for which we propose a set of word-based and character-based features. Inexpensive, robust and efficient, our method achieves an average F1 score of nearly 0.7 with the existence of much noise in our annotated Twitter data. In view of the deficiency of training data for the task, we experimented extensively with different configurations of training and test data, leading to empirical findings that may provide valuable reference for building benchmark datasets for sustained research on speech act recognition in Twitter.