Sentiment Analysis and Social Computing
GUAN Saiping, JIN Xiaolong, XU Xueke, WU Dayong, JIA Yantao, WANG Yuanzhuo, LIU Yue
2017, 31(5): 203-214.
With the rapid development of news websites, the news comments increase sharply, which are very important to public opinion analysis and news comments recommendation. This paper proposes a news comments clustering method, called EWMD-AP, to automatically mine the focuses of the public on the news. This method employs Word Mover's Distance (WMD) with enhanced weight vectors to calculate the distances between news comments. It also adopts Affinity Propagation (AP) to cluster comments, and finally obtains the clusters and their representative comments corresponding to the focuses of the public. Particularly, this paper proposes to replace the traditional word frequency based weight vectors in WMD with enhanced weight vectors, which consist of three components: the importance coefficient of words, the de-contextualization coefficient, and the traditional TFIDF coefficient. Experimental results on 24 news comments datasets demonstrate that EWMD-AP performs much better than both traditional clustering methods (e.g. Kmeans, Mean Shift, etc) and the state-of-the-art ones (e.g. Density Peaks, etc).