Simultaneous Identification of Tweet Purpose and Position
This work addresses a specific challenge in social media analysis by improving classification accuracy for tweets, though it is incremental as it builds on existing multi-label methods.
The paper tackles the problem of simultaneously classifying tweet purpose and user position, transforming it into a multi-label classification task with post-processing, and shows that this method outperforms individual classification approaches on real-world datasets.
Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and sentiment classification, which classifies tweets into positive, negative and neutral. Since tweets are different from conventional text in that they generally are of limited length and contain informal, irregular or new words, so it is difficult to determine user intention to publish a tweet and user attitude towards certain topic. In this paper, we aim to simultaneously classify tweet purpose, i.e., the intention for user to publish a tweet, and position, i.e., supporting, opposing or being neutral to a given topic. By transforming this problem to a multi-label classification problem, a multi-label classification method with post-processing is proposed. Experiments on real-world data sets demonstrate the effectiveness of this method and the results outperform the individual classification methods.