[Corpora-List] Call for participation: SemEval Task 11 -- Sentiment Analysis of Figurative Language in Twitter

[Ekaterina Shutova]

TASK11 at SEMEVAL-2015: Sentiment Analysis of Figurative Language in Twitter

One of the most difficult problems when assigning either positive or
negative polarity in sentiment analysis tasks is to accurately
determine what is the truth value of a certain statement. In the case
of literal language, when there is not a secondary meaning, existing
techniques already achieve good results. However, in case of
figurative language such as irony and affective metaphor, when
secondary or extended meanings are intentionally profiled, the
affective polarity of the literal meaning may contrast sharply with
the affect created by the figurative meaning. Nowhere is this effect
more pronounced than in ironic or sarcastic language, which delights
in using affirmative language to convey negative meanings. Metaphor,
irony and figurative language more generally demonstrate the limits of
conventional techniques for the sentiment analysis of literal texts.

So figurative language creates a significant challenge for a sentiment
analysis system, as direct approaches based on words and their lexical
semantics are often shown to be inadequate in the face of indirect
meanings. It would be convenient then if such language were rare and
confined to specific genres of text, such as poetry and literature.
Yet the reality is that figurative language is pervasive in almost any
genre of text, and is especially commonplace on the texts of the Web
and in social media communications. Figurative language often draws
attention to itself as a creative artifact, but is just as likely to
be viewed as part of the general fabric of human communication. In any
case, Web users employ figures of speech (both old and new) to project
their personality through a text, especially when limited to the 140
characters of a tweet.

This significant challenge is the basis for SemEval Task 11, for which
trial and training data are now available!

The task: given a set of tweets that are rich in irony and metaphor,
the goal is to determine whether the user has expressed a positive,
negative or neutral sentiment. A fine-grained sentiment scale (scores
in the range -5...+5) is be used to capture the effect of irony and
metaphor on the perceived sentiment of a tweet. Participating systems
will have to assign sentiment scores from the same fine-grained scale
to a set of given tweets.

Trial (1000 figurative tweets) and training data  (8000 figurative
tweets): annotated and available at
http://alt.qcri.org/semeval2015/task11

Important dates:
December 5, 2014         Evaluation starts
December 20, 2014        Evaluation ends
January 30, 2015         Paper submission due
February 28, 2015        Paper reviews due
March 30, 2015           Camera-ready papers due
Summer 2015              SemEval workshop

Contact:  tony.veale at ucd.ie

Organizers:

        Tony Veale, University College Dublin
        John Barnden, University of Birmingham
        Antonio Reyes, ISIT
        Paolo Rosso, Univiversitat Politècnica de València
        Ekaterina Shutova, UC Berkeley

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