基于情感-主题-讽刺混合模型的讽刺检测研究

Study on Satire Detection Based on Sentiment-Topic-Satire Hybrid Model

讽刺检测是观点挖掘的一个子任务,主要目的是识别用户在书面文本中表达的观点或情感。文本中讽刺句往往具有混合的情感极性,正确识别讽刺句和非讽刺句在情感分析中起着至关重要的作用。讽刺检测方法一般都采用机器学习分类器,其中分类器的训练主要基于简单的词汇或基于词典的特征。本研究的目的是建立一个无监督的概率关系模型,根据微博中词语的情感分布来识别讽刺主题。模型基于主题级分布估计相关情感,评估出现在短文本中的情感相关词,给出情感相关标签。实验结果表明,该模型在讽刺检测方面优于其他最新的基线模型,非常适合于短文本的讽刺预测。

Satire detection is a subtask of opinion mining.Its main purpose is to identify the opinion or emotions expressed by users in written texts.Satirical sentences in texts often have mixed sentiment polarity.Correctly identifying satirical sentences and non satirical sentences plays an important role in sentiment analysis.Various satire detection methods are based on machine lear-ning classifiers,in which the training of classifiers is mainly based on simple words or dictionary features.The purpose of this studyis to establish an unsupervised probabilistic relationship model to identify satirical themes according to the sentiment distribution of words in microblog.The model estimates the related sentiment based on the topic level distribution,evaluates the sentiment related words appearing in the short text,and gives the sentiment related labels.Experimental results show that the model is superior to other latest baseline models in satire detection,and is very suitable for satire prediction of short text.