Unsupervised Opinion Phrase Extraction and Rating in Chinese Blog Posts
Jenq‐Haur Wang, Chi‐Ching Lee · 2011
Opinion analysis has become important since there're huge amount of opinions and comments in user-generated content for social media such as blogs and forums. While machine learning methods classify opinions by statistical properties such as term frequency, lexicon-based methods could reflect the intricate semantics in determining the opinion orientation and strength. In this paper, we propose an unsupervised opinion phrase extraction and rating mechanism with opinion lexicons. First, opinion phrases are extracted from each blog post by matching terms in the opinion lexicon. Then, scores are assigned to each opinion phrase for different scales of opinion strength. Finally, the total score for the post is estimated by averaging all of its opinion phrases. From the experimental results on digital camera posts, the proposed method achieved high accuracy with sigmoid scoring. This shows effective extraction of opinion phrases and rating of opinion scales. Further investigation is needed to test the effectiveness in different document domains.