text: Subjective questions such as does neymar dive', or is clinton lying', or is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.
  @cite_4: Even with the recent developments in Web search of answering queries from structured data, search engines are still limited to queries with an objective answer, such as EUROPEAN CAPITALS or WOODY ALLEN MOVIES. However, many queries are subjective, such as SAFE CITIES, or CUTE ANIMALS. The underlying knowledge bases of search engines do not contain answers to these queries because they do not have a ground truth. We describe the Surveyor system that mines the dominant opinion held by authors of Web content about whether a subjective property applies to a given entity. The evidence on which SURVEYOR relies is statements extracted from Web text that either support the property or claim its negation. The key challenge that SURVEYOR faces is that simply counting the number of positive and negative statements does not suffice, because there are multiple hidden biases with which content tends to be authored on the Web. SURVEYOR employs a probabilistic model of how content is authored on the Web. As one example, this model accounts for correlations between the subjective property and the frequency with which it is mentioned on the Web. The parameters of the model are specialized to each property and entity type. Surveyor was able to process a large Web snapshot within a few hours, resulting in opinions for over 4 billion entity-property combinations. We selected a subset of 500 entity-property combinations and compared our results to the dominant opinion of a large number of Amazon Mechanical Turk (AMT) workers. The predictions of Surveyor match the results from AMT in 77 of all cases (and 87 for test cases where inter-worker agreement is high), significantly outperforming competing approaches.
summary: That is related with irony, which is a particular challenge for sentiment analysis that requires advanced techniques. In fact, the problem of detecting claims on a specific property for an entity is a somewhat different one, which is endowed with a pre-defined conceptual structure (see ) and really seems to be better addressed directly by dependency parse trees. The preference of the latter over sentiment analysis techniques for the claim parsing and polarity detection task is discussed by @cite_4 .