基于主题模型的微博评论方面观点褒贬态度挖掘

Aspect-Based Opinion and Affective Meaning in Microblogging Comments Via Topic Model

在新浪微博中,原创微博下存在着很多用户评论。这些评论能反映原创微博的内容,用户的态度以及与其相关的一些话题。因此,对这些评论进行细粒度信息的提取与褒贬态度的分类很有必要。基于上述原因,该文首先提出与原创无关的评论判别方法,通过三个相似度方法得到原创微博与评论之间的相关度,从而判断评论对象是否为原创微博。其次,提出一种用于评论集褒贬态度和方面观点挖掘的新模型,该模型在LDA中加入了表情符号层与文本情感层,实现评论集方面和褒贬态度的同步检测。实验表明:表情符号情感层的融入能提高新模型褒贬态度识别能力。

In microblogging site,the comments under the original microblog reflect the original microblog’s content,users’ attitudes and certain related topics.To extract fine-grained information and affective meaning from those comments,we first propose to detect if a comment is targeted towards the microblog itself using three similarity methods.Then,a novel model is proposed for mining aspect-based opinion and affective meaning in microblogging comments.This model introduces emoticon sentiment and textual sentiment into LDA inference framework and achieves synchronized detection of aspect and affective meaning in comments.Experimental results demonstrate that the emoticon sentiment layer can improve the affective meaning recognition results.