The Challenge of Processing Opinions in Online Contents in the Social Web Era

In the past years, the NLP community has been increasingly interested in the field of opinion mining (also known as sentiment analysis), whose aim is to retrieve and classify the opinions expressed in text. Online reputation management, as a related task, is more focused on opinions on individuals and other entities. Additionally, the computational task of online reputation management also considers the analysis of facts that influence the status quo of these entities. The problem in this context is much more difficult to solve, as entities, as opposed to products, are related to different events and topics and there is no fixed set of “attributes” that are commented on by persons expressing opinions on these entities. There is only one freely accessible system performing such as a task - Lydia (Skiena et al., 2007), which gathers news from portals and blogs and classifies opinions on different entities. However, both this system, as well as different approaches that have been presented for this problem in the research literature, show that the issue of entity-centered opinion mining and, additionally, the correlation of the results with facts over events where these entities are involved are not trivial (Balahur and Steinberger, 2009; Zhang and Skiena, 2010). The present position paper studies the challenges related to the field of online reputation management and suggests possible solutions.

BALAHUR DOBRESCU Alexandra; 

2013-01-21

European Language and Resources Association

JRC70037

https://publications.jrc.ec.europa.eu/repository/handle/JRC70037,