@inproceedings{maua2009enia,
 abstract = {A large number of user reviews in the internet
contains valuable information on services and products;
for this reason, there is interest in automatically
understanding such reviews. Sentiment Classification
labels documents according to the feelings they
express; instead of classifying a document into topics
(sports, economics, etc), one attempts to tag the
document according to overall feelings. Compared to the
accuracy of traditional text categorization methods,
sentiment classifiers have shown poor performance. We
argue that such bad results are due to an improper
representation of reviews. We describe a weakly
supervisedmethod that converts raw text into an
appropriate represen- tation, and show how techniques
from information retrieval can acquire labeled data and
process data using Markov logic. We report results on
sentence classification and rating prediction that
support our claims.},
 address = {Bento Gonçalves, Brazil},
 author = {Denis Deratani Mauá and Fabio Gagliardi Cozman},
 booktitle = {Encontro Nacional de Inteligência Artificial},
 keywords = {markov logic,text categorization},
 title = {Representing and Classifying User Reviews},
 url = {http://www.ime.usp.br/~logprob/articles/maua-ENIA2009.pdf},
 year = {2009}
}