TY  - JOUR
T1  - A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
AU  - Ji, Seonmi 
AU  - Moon, Jihoon 
AU  - Kim, Hyeonwoo 
AU  - Hwang, Eenjun 
JO  - Journal of KIISE, JOK
PY  - 2018
DA  - 2018/1/14
DO  - 10.5626/JOK.2018.45.10.1045
KW  - twitter news classification
KW  - semantic enrichment of word features
KW  - machine learning
KW  - artificial neural network
AB  - Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.