Within the area of general-purpose finegrained subjectivity analysis, opinion topic identification has, to date, received little attention due to both the difficulty of the task and the lack of appropriately annotated resources. In this paper, we provide an operational definition of opinion topic and present an algorithm for opinion topic identification that, following our new definition, treats the task as a problem in topic coreference resolution. We develop a methodology for the manual annotation of opinion topics and use it to annotate topic information for a portion of an existing general-purpose opinion corpus. In experiments using the corpus, our topic identification approach statistically significantly outperforms several non-trivial baselines according to three evaluation measures.
Later REF argue that in fine grained subjectivity analysis, topic identification is very relevant, and treat the task from the perspective of topic coreference resolution.
Topic Identification for Fine-Grained Opinion Analysis
International Conference On Computational Linguistics
Computer Science