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We present a fully automatic and weighted dictionary to be used in topical opinion retrieval. We also define a simple topical opinion retrieval function that is free from parameters, so that the retrieval does not need any learning or tuning phase.
Freshness of information in real-time search is central in social networks, news, blogs and micro-blogs. Nevertheless, there is not a clear experimental evidence that shows what principled approach effectively combines time and content. We introduce a novel approach to model freshness using a survival analysis of relevance over time. In such models,… (More)
We investigate the effectiveness of both the standard evaluation measures and the opinion component for topical opinion retrieval. We analyze how relevance is affected by opinions by perturbing relevance ranking by the outcomes of opinion-only classifiers built by Monte Carlo sampling. Topical opinion rankings are obtained by either re-ranking or filtering… (More)
We present a method to automatically generate a term-opinion lexicon. We also weight these lexicon terms and use them at real time to boost the ranking with opinionated-content documents. We define very simple models both for opinion-term extraction and document ranking. Both the lexicon model and retrieval model are assessed. To evaluate the quality of the… (More)