Classifying Tweets using Convolutional Neural Networks with Multi-Channel Distributed Representation
Classifying Tweets using Convolutional Neural Networks with Multi-Channel Distributed Representation
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发表时间：
2018
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通讯作者：
Shuichi Hashida;Keiichi Tamura;Tatsuhiro Sakai
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作者：
Shuichi Hashida;Keiichi Tamura;Tatsuhiro Sakai
本文主要研究了一种最新的短信分类方法。随着人们对社交媒体的兴趣与日俱增，人们发布了许多短信，不仅是为了与他人交流，也是为了分享信息。这一趋势催生了一个新的研究领域，微博挖掘。这种类型的数据挖掘可以从微博中提取现实世界的主题和事件。然而，由于微博网站上的短信很短，对它们的分类是一项具有挑战性的任务。特别值得一提的是，Twitter是最知名的微博服务之一，它经常发布用户对现实世界中发生的话题和事件的反应。在我们的上一篇论文中，我们提出了一个实时话题监控系统，该系统实现了一个朴素贝叶斯分类器，将推文分为两类：与被监控话题相关的和不相关的。由于朴素贝叶斯分类是基于词的概率进行分类，因此分类性能存在局限性。为了解决这个问题，我们提出了一种基于深度学习的分类方法，该方法采用了一种新的分布式词语表示法--多通道分布式表示法。分布式表征指的是代表词语潜在特征的词语向量。为了增强分布式表示的能力，其每个项在多通道分布式表示中具有多个通道值。在我们的实验中，我们与其他卷积神经网络(CNN)模型和长短期记忆模型进行了比较，评估了我们的模型的性能。结果表明，深度学习模型的分类性能优于朴素贝叶斯分类器。此外，具有多通道分布式表示的CNN比没有多通道分布式表示的CNN能够更好地对推文进行分类。
This paper is focused on a state-of-art classification method for short text messages. With the increasing interest in social media, people are posting many short text messages, not only to communicate with other people, but also to share information. This trend has led to a new research area, microblog mining. This type of data mining can extract realworld topics and events from microblogs. However, because text messages on a micro-blogging site are short, their classification is a challenging task. In particular, Twitter is one of the most well-known micro-blogging services, where tweets frequently users’ reactions to real-world topics and events occurring in their surroundings. In our previous paper, we proposed a realtime topic monitoring system that implements a naive Bayes classifier to classify tweets into two classes: “relevant” and “irrelevant” to a monitored topic. The classification performance has limitations, because the naive Bayes’ classification is based on word probabilities. To address this problem, we propose a deep-learning-based classification method that features a new distributed representation for words, multichannel distributed representation. Distributed representation indicates word vectors representing the latent features of words. To enhance the capability of a distributed representation, each of its items has several channel values in a multi-channel distributed representation. In our experiments, we evaluated our model’s performance in comparison with that of other convolutional neural network (CNN) models and a long shortterm memory model. The results showed that the classification performance of the deep learning models was superior to that of the naive Bayes classifier. Moreover, a CNN with multi-channel distributed representation can classify tweets better than a CNN without multi-channel distributed representation.