Extracting Topic-related Opinions and their Targets in NTCIR-7.
Youngho Kim, Seongchan Kim, Sung-Hyon Myaeng · 2008
In recent years, there have been many interests in opinion resources such as online news, blogs, and forums. With this tendency, many opinionrelated applications are proposed to quench an opinion-seeking desire of users. However, selecting publicly interesting opinions is important to improve the effectiveness of such opinion applications, by focusing more on important opinions. To achieve this goal, we propose an opinion mining system which extracts topic-related opinions (at sentence level) and identifies their targets. Our system can be characterized with probabilistic divergence based keyword extraction, language model based topic relevance determination with web-snippets expansion, and heuristic feature based target identification. Experimental results show that our approach is promising.