[SIGLEX-MWE] CFP: SemEval 2015 Task 11 -- Sentiment Analysis of Figurative Language in Twitter

[Ekaterina S. <ka...@ic...>]

SemEval Task 11: 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:  ton...@uc...

Organizers:

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