Adding Twitter-Specific Features to Stylistic Features for Classifying Tweets by User Type and Number of Retweets
Adding Twitter-Specific Features to Stylistic Features for Classifying Tweets by User Type and Number of Retweets
复制标题
DOI：
10.1002/asi.23126
复制
发表时间：
2014-07-01
影响因子：
3.5
通讯作者：
Suzuki, Takafumi
中科院分区：
文献类型：
--
作者：
Arakawa, Yui;Kameda, Akihiro;Suzuki, Takafumi
最近，Twitter作为一种新的信息传播方式，受到了公众和研究人员的广泛关注。其中，转推（RT）和用户类型的数量是了解Twitter上的信息传输的两个重要分析项目。为了分析这一点，我们应用了文本分类和特征提取实验，使用随机森林机器学习与传统的风格和Twitter特定的功能。我们首先从40个拥有大量粉丝的账户中收集了推文，并从28，756条推文中创建了推文文本。然后，我们进行了15种类型的分类实验，使用各种组合的功能，如功能词，语音术语，Twitter的描述性语法，和信息的角色。我们特意观察了特征对分类性能的影响。结果表明，类分类每个用户表示最好的性能。此外，我们观察到某些特征对分类有更大的影响。在评估RT数量水平的实验中，信息角色产生了影响。在用户实验的情况下，重要的特征，如敬语后置助词和助动词，如“desu”和“masu”，产生了影响。这项研究阐明了根据RT和用户类型的数量对推文进行分类有用的功能。
Recently, Twitter has received much attention, both from the general public and researchers, as a new method of transmitting information. Among others, the number of retweets (RTs) and user types are the two important items of analysis for understanding the transmission of information on Twitter. To analyze this point, we applied text classification and feature extraction experiments using random forests machine learning with conventional stylistic and Twitter-specific features. We first collected tweets from 40 accounts with a high number of followers and created tweet texts from 28,756 tweets. We then conducted 15 types of classification experiments using a variety of combinations of features such as function words, speech terms, Twitter's descriptive grammar, and information roles. We deliberately observed the effects of features for classification performance. The results indicated that class classification per user indicated the best performance. Furthermore, we observed that certain features had a greater impact on classification. In the case of the experiments that assessed the level of RT quantity, information roles had an impact. In the case of user experiments, important features, such as the honorific postpositional particle and auxiliary verbs, such as "desu" and "masu," had an impact. This research clarifies the features that are useful for categorizing tweets according to the number of RTs and user types.