Two-side Feature Clustering Using Auxiliary Vector for Improving Stance Classification on Chinese Newspaper
Date Issued
2016
Date
2016
Author(s)
Chen, Wei-Ming
Abstract
In order to relieve media bias problem and selective preference problem, we aim at developing an intelligent system to classify the stance of Chinese news article on several controversial topics. We proposed a simple and efficient approach which can incorporate the information of unlabeled news corpus and the information of training data to merge similar features. In our approach, features were divided into two sides according to initial training process, and word2vec tool was utilized to produce auxiliary vectors for each feature. Finally, fast community detection algorithm was applied for clustering similar features. Experimental results show that our approach outperforms raw features and common dimensionality reduction techniques in most cases.
Subjects
stance classification
stance classification on Chinese newspaper
feature clustering
natural language processing
machine learning
Type
thesis
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