Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose

One of the reasons sentiment lexicons do not reach human-level performance is that they lack the contexts that define the polarities of words. While obtaining this knowledge through machine learning would require huge amounts of data, context is commonsense knowledge for people, so human computation is a better choice. We identify context using a game with a purpose that increases the workers' engagement in this complex task. With the contextual knowledge we obtain from only a small set of answers, we already halve the sentiment lexicons' performance gap relative to human performance.


Editor(s):
Gelbukh, A
Published in:
Computational Linguistics And Intelligent Text Processing, Cicling 2014, Part Ii, 8404, 32-44
Presented at:
15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Kathmandu, NEPAL, APR 06-12, 2014
Year:
2014
Publisher:
Berlin, Springer-Verlag Berlin
ISSN:
0302-9743
ISBN:
978-3-642-54902-1
978-3-642-54903-8
Laboratories:




 Record created 2014-11-13, last modified 2018-04-29

Preprint:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)