中文摘要：

为了过滤Web论坛中的低质量回帖，提出了一种新的基于LDA（1atcntDirichletallocation）的低质量回帖检测方法．不同于以往的方法，该方法在对回帖进行质量分类时使用了两类特征：语义特征和统计特征．提出并定义了垃圾阳乍重要（J／I）主题比例、主题不确定度和主题相关度3种语义特征．为克服TF·IDF方法在表示稀疏文本语义上的局限性，语义特征在LDA主题空间上计算．另外，统计特征包括浅层特征、句法特征和论坛专有特征．由于检测回帖质量可被看作二元分类问题，训练SVM分类器来区分出低质量回帖．在3个不同数据集上的实验结果表明，新方法在精确率、查全率和F1测度上均优于已知的方法．

英文摘要：

Web forum is one of the major types of social media in Web 2. 0. However, the generated contents in Web forums can vary in quality, ranging from excellent detailed opinions to topic drift contents or swear words. Therefore, a novel LDA （latent Dirichlet allocation） based approach is proposed in this paper to detect low-quality posts in Web forums. Compared with previous methods, the new one uses both semantic and statistic features of a post to evaluate its quality. The semantic features include Junk/Insignificant （J/I） topic proportion, topic uncertainty and topic relevance, which are computed in LDA topic space in order to overcome the ineffectiveness of TF · IDF based features in short texts. An LDA model is firstly built to predict the topic distribution of each post. Then, semantic features of a post are computed based on its topic distribution. The statistic features contain surface, syntactic and forum specific features of posts, which are selected based on the analysis of the posts＇ contents. Since detecting the low-quality posts can be considered as a bi-classification problem, SVM is used to filter the low-quality posts. Experimental results on three different datasets show that the new approach outperforms the previous ones in terms of precision, recall and F~ values.