Mining Opinion Polarity Relations of Citations

Opinion mining has been receiving increasing attention recently, and various approaches have been suggested for mining sentiment information, such as mining attitudes or opinions about a topic or product etc. However, as far as we know, little work has been reported on citation opinion mining (COM). By COM, we refer to the process of identifying authors opinions towards the works they cite, such as positive/negative attitudes or approval/disapproval. We contend that such information is useful for semantic information retrieval and text mining, particularly for users who wish to search for documents taking a positive or negative stance towards a specific previous work. In this paper, we propose a system which is based on existing semantic lexical resources and NLP tools, aiming to create a network of opinion polarity relations between documents and citations. This is a web-based system which allows users to access the citations collected from documents and retrieve those documents linked to each of the citations with different opinion polarity relations, namely approval, neutral or disapproval relations. Various approaches will be tested including detecting semantic orientation of subjective words in the context of citations and machine learning using manually annotated data. In particular, we will explore the use of semantic lexicons for this task.