Exploiting Social Debates for Opinion Ranking

The number of opinions in news media platforms is increasing dramatically with daily news hits, and people spending more and more time to discuss topics and share experiences. Such user generated content represents a promising source for improving the effectiveness of news articles recommendation and retrieval. However, the corpus of opinions is often large and noisy making it hard to find prominent content. In this paper, we tackle this problem by proposing a novel scoring model that ranks opinions based on their relevance and prominence. We define the prominence of an opinion using its relationships with other opinions. To this end, we (1) create a directed graph of opinions where each link represents the sentiment an opinion expresses about another opinion (2) propose a new variation of the PageRank algorithm that boosts the scores of opinions along links with positive sentiments and decreases them along links with negative sentiments. We have tested the effectiveness of our model through extensive experiments using three datasets crawled from CNN, Independent, and The Telegraph Web sites . The experiments show that our scoring model achieves high quality results.