Sentiment Analysis in Social Media Texts
This paper presents a method for sentiment
analysis specifically designed to work with
Twitter data (tweets), taking into account their
structure, length and specific language. The
approach employed makes it easily extendible
to other languages and makes it able to process
tweets in near real time. The main contributions
of this work are: a) the pre-processing
of tweets to normalize the language and generalize
the vocabulary employed to express sentiment;
b) the use minimal linguistic processing,
which makes the approach easily portable
to other languages; c) the inclusion of higher
order n-grams to spot modifications in the polarity
of the sentiment expressed; d) the use of
simple heuristics to select features to be employed;
e) the application of supervised learning
using a simple Support Vector Machines
linear classifier on a set of realistic data. We
show that using the training models generated
with the method described we can improve
the sentiment classification performance, irrespective
of the domain and distribution of the
test sets.
BALAHUR DOBRESCU Alexandra;
2014-08-18
Association for Computational Linguistics
JRC82012
978-1-937284-47-3,
http://www.aclweb.org/anthology/W13-1617,
https://publications.jrc.ec.europa.eu/repository/handle/JRC82012,
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