情报杂志Issue(3)：161-164,4.DOI:10.3969/j.issn.1002-1965.2014.03.030
基于LDA的网络评论主题发现研究
Topic Extraction Research of Net Reviews Based on Latent Dirichlet Allocation
阮光册1
作者信息
- 1. 华东师范大学商学院信息学系 上海 200241
- 折叠
摘要
Abstract
Topic extraction of web user opinions is an important way of web 2. 0 information analysis. How to analyze valuable informa-tion from miscellaneous user opinions is a challenging issue. Due to short information content and amount of web user opinions, the article put forward information analysis method based on Latent Dirichlet Allocation and HowNet knowledge base to extract net review topic. Firstly, to set up the corpus through textual tagging and semantic analysis of the reviews, then using HowNet to calculate semantic similari-ty of the corpus items and to reduce semantic repetition, finally, using Latent Dirichlet Allocation to map the topic and realize new review topic extraction.关键词
网络评论/主题发现/网络信息分析/LDA( latent Dirichlet allocation)/语义分析/文本挖掘Key words
web review/topic extraction/web information analysis/LDA( Latent Dirichlet Allocation)/semantic analysis/text min-ing分类
社会科学引用本文复制引用
阮光册..基于LDA的网络评论主题发现研究[J].情报杂志,2014,(3):161-164,4.
