By Topic

How Much Supervision? Corpus-Based Lexeme Sentiment Estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wawer, A. ; Inst. of Comput. Sci., Warsaw, Poland ; Rogozinska, D.

This paper is focused on comparing corpus-based methods for estimating word sentiment. Evaluated algorithms represent varying degrees of supervision and range from regression alike approaches to more heavily supervised classifications. The main idea is to explore the opportunities arising from mining medium sized, balanced corpora - as opposed to web as a corpus paradigm. The comparisons have been carried using sentiment estimator benchmarks designed to take into account classification and regression problems as well as varying granularity of predicted sentiment scores: from simple to complex scales. Overall, the results turn out to be very promising and indicate superiority of supervised algorithms, especially for lower sentiment granularity predictions. However, unsupervised methods can be still considered as an interesting alternative in the case of the most fine-grained, regression like scenarios of sentiment estimation. In these cases heavy supervision and large number of features are less attractive than simple unsupervised methods.

Published in:

Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on

Date of Conference:

10-10 Dec. 2012