A scalable approach for Contradiction Detection driven by Opinion mining

Abstract

In this paper we address the problem of identifying contradictions by opinion mining across documents. Our approach involves opinion extraction and storage by processing natural language documents such as reviews, news etc. and aims the identification of contradictory opinions related to the same target expressed by the same holder or by different holders. By matching the structured representations of opinions we identify a potential inconsistency occurring in two documents that is signaled and further analysis is applied to confirm/infirm the contradiction. Moreover, communities are detected both on individual opinions and social data. Thus, the (in)consistency might be tracked for the holder as a member of a community, as well as for the holder as an individual. We addressed scalability by designing a cloudbased storage infrastructure and an efficient indexing system that allows for fast retrieval and matching of structured representations.

A Social Networking Platform for Semantic Time Series Processing

“…For example, contradiction extraction and opinion mining could be used to implement a more advanced rating system for time series resources. A scalable approach is introduced in [5]. Context based interest reflection as in [12] could be also used to find out more about common interests of users.…”