论文标题
同时识别推文目的和位置
Simultaneous Identification of Tweet Purpose and Position
论文作者
论文摘要
推文分类最近引起了很大的关注。推文分类的大多数现有工作都集中在主题分类上，该分类将推文分为几个预定义的类别，以及情感分类，这些分类将推文分类为正，负和中立。由于推文与传统文本不同，因为它们的长度通常有限并且包含非正式，不规则或新单词，因此很难确定用户意图以发布推文和用户对某些主题的态度。在本文中，我们旨在同时对推文目的进行分类，即用户发表推文和位置的意图，即支持，反对或对给定主题中立。通过将此问题转换为多标签分类问题，提出了一种具有后处理的多标签分类方法。对现实世界数据集的实验证明了该方法的有效性，结果表现优于单个分类方法。
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.
