Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences.
Emmanuel Ebuka Ibeke, Chenghua Lin, Adam Wyner, Mohamad Hardyman Barawi · International Joint Conference on Natural Language Processing · 2017
Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts. Most existing studies separate input data into respective collections. In addition, the relationships between the topics extracted and the sentences in the corpus which express the topics are opaque, hindering our understanding of the opinions expressed in the corpus. We propose a novel unified latent variable model (contraLDA) which addresses the above matters. Experimental results show the effectiveness of our model in mining contrasted opinions, outperforming our baselines.