Although sentiment analysis has a strong history of success on customer feedback and certain blogs and editorials, accuracy results are mixed for data in the absence of an opinion holder. In particular, news data poses some unique challenges to accuracy for sentiment analysis due to the blending of what I will call "objective" polarity with opinion-based polarity. How is (document-level) Sentiment to be determined, for example, in an article about the Haitian earthquake that discusses humanitarian aid? Similarly, an article about Bernard Madoff’s jail sentence shows a highly negative “objective polarity” somehow mitigated by a subsequent action. And how can we tease an author’s opinion from the semantics of objective polarity where they exist in news data? Author opinion (often referred to as “bias”) in news data is subtle in its indication by design. This talk discusses the grounding of the concept of "sentiment" within the greater context of the Semantics of Opposition.
Clipping is a handy way to collect important slides you want to go back to later.