基于多特征融合Single-Pass-SOM组合模型的话题检测

当今时代, 网络舆情传播速度快、影响力大, 而话题检测在网络舆情监管中有着不可替代的作用. 针对传统方法提取文本特征不完整和特征维度过高的问题, 本文提出了基于时间衰减因子的LDA&&Word2Vec文本表示模型, 将LDA模型的隐含主题特征和Word2Vec模型的语义特征进行加权融合, 并引入了时间衰减因子, 同时起到了降维和提高文本特征完整度的作用. 同时, 本文又提出了Single-Pass-SOM组合聚类模型, 该模型解决了SOM模型需要设定初始神经元的问题, 提高了话题聚类的精度. 实验结果表明, 本文提出的文本表示模型和文本聚类方法较传统方法拥有更好的话题检测效果.

Topic Detection of Single-Pass-SOM Combination Model Based on Multi Feature

Nowadays, internet public opinion has a rapid spread and great influence, and topic detection plays an irreplaceable role in the supervision of public opinion. Aiming at the problems of incomplete feature extraction and high feature dimension in traditional methods, this study proposes LDA&&Word2Vec text representation model based on time decay factor, which combines the hidden subject features by LDA model with the semantic features by Word2Vec model, and adds time decay factor, which can reduce the dimension and improve the integrity of text features. At the same time, this study proposes a Single-Pass-SOM clustering model, which solves the problem of setting initial neurons in SOM model, and improves the accuracy of topic clustering. Experimental results show that the text representation model and text clustering method proposed in this study have better topic detection effect than traditional methods.