Enhancing Classification Effectiveness of Chinese News Based on Term Frequency

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI:10.1109/SC2.2017.26

Tzu-Yi Chan, Yue-Shan Chang

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引用次数: 4

Abstract

For the daily news published on the web, in general, they can be classified into various categories, such as social, politics, entertainment, and so on. These classifications motivate users to watch the desired information. If the classification is wrong, user cannot catch accurately context. How to accurately classify the daily news is becoming an important issue. In this paper, we will propose a method to enhance the effectiveness of news classification. We will utilize the term frequency appeared in variety of classified historical news to training the weighting of each category of each term. And then classify the test news based on the weighting. We propose a framework and an algorithm to training the weighting of each term. The training data, which are over 3500 Chinese news, are collected from UDN and LTN, which are two major electrical news portals in Taiwan. Based on the weighting mechanism, we conduct some experiments to evaluate the effectiveness of the algorithm. The test data are 170 Chinese news, which are collected from Google. The result shows that the traditional manually classification method has up to 13% error classification.

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基于词频的中文新闻分类有效性提升

对于每天在网络上发布的新闻，一般来说，它们可以分为各种类别，如社会，政治，娱乐等。这些分类激励用户观看所需的信息。如果分类错误，用户就不能准确地捕捉上下文。如何对日常新闻进行准确分类成为一个重要的问题。本文将提出一种提高新闻分类有效性的方法。我们将利用各种分类历史新闻中出现的词汇频率来训练每个词汇的每个类别的权重。然后根据权重对测试新闻进行分类。我们提出了一个框架和一种算法来训练每个项的权重。训练数据来自台湾两大电子新闻门户UDN和LTN，共3500余条中文新闻。基于加权机制，我们进行了一些实验来评估算法的有效性。测试数据为170条中文新闻，来自谷歌。结果表明，传统的人工分类方法的分类误差高达13%。

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2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)

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