关键词: 舆情分析, 短文本, 增量聚类算法, 向量空间模型, 神经网络

Abstract: Incremental clustering algorithms for news comments can effectively discover the views of netizens on the news event, which is of great significance in the field of public opinion analysis. The traditional algorithms for incremental clustering short texts are sensitive to the input sequence, this paper proposes an improved UCSP (uncertain cyclic Single-Pass) incremental clustering algorithm. In the process of clustering, the traditional vector space model for short texts is lack of semantic information, and has the disadvantage of sparse feature. Combined with neural network vector model, this paper constructs a new representation model for short texts based on compositional semantic features. Compared with the traditional texts representation models and clustering algorithms on 5 Tencent news comments data sets, the results show that the proposed algorithm can more effectively improve the quality of clustering.

Key words: public opinion analysis, short texts, incremental clustering algorithm, vector space model, neural network