The task of extracting the opinion expressed in
text is challenging due to different reasons.
One of them is that the same word (in particular, adjectives) can have different polarities
depending on the context. This paper presents
the experiments carried out by the OpAL team
for the participation in the SemEval 2010 Task
18 – Disambiguation of Sentiment Ambiguous
Adjectives. Our approach is based on three different strategies: a) the evaluation of the polarity of the whole context using an opinion mining system; b) the assessment of the polarity of
the local context, given by the combinations
between the closest nouns and the adjective to
be classified; c) rules aiming at refining the local semantics through the spotting of modifiers. The final decision for classification is taken according to the output of the majority of
these three approaches. The method used
yielded good results, the OpAL system run
ranking fifth among 16 in micro accuracy and
sixth in macro accuracy
%0 Conference Proceedings
%1 balahur2010applying
%A Balahur, Alexandra
%A Montoyo, Andrés
%D 2010
%K disambiguation mining opinion sense word
%T OpAL: Applying Opinion Mining Techniques for the Disambiguation of
Sentiment Ambiguous Adjectives in SemEval-2 Task 18
%U http://aclweb.org/anthology-new/S/S10/S10-1099.pdf
%X The task of extracting the opinion expressed in
text is challenging due to different reasons.
One of them is that the same word (in particular, adjectives) can have different polarities
depending on the context. This paper presents
the experiments carried out by the OpAL team
for the participation in the SemEval 2010 Task
18 – Disambiguation of Sentiment Ambiguous
Adjectives. Our approach is based on three different strategies: a) the evaluation of the polarity of the whole context using an opinion mining system; b) the assessment of the polarity of
the local context, given by the combinations
between the closest nouns and the adjective to
be classified; c) rules aiming at refining the local semantics through the spotting of modifiers. The final decision for classification is taken according to the output of the majority of
these three approaches. The method used
yielded good results, the OpAL system run
ranking fifth among 16 in micro accuracy and
sixth in macro accuracy
@proceedings{balahur2010applying,
abstract = {The task of extracting the opinion expressed in
text is challenging due to different reasons.
One of them is that the same word (in particular, adjectives) can have different polarities
depending on the context. This paper presents
the experiments carried out by the OpAL team
for the participation in the SemEval 2010 Task
18 – Disambiguation of Sentiment Ambiguous
Adjectives. Our approach is based on three different strategies: a) the evaluation of the polarity of the whole context using an opinion mining system; b) the assessment of the polarity of
the local context, given by the combinations
between the closest nouns and the adjective to
be classified; c) rules aiming at refining the local semantics through the spotting of modifiers. The final decision for classification is taken according to the output of the majority of
these three approaches. The method used
yielded good results, the OpAL system run
ranking fifth among 16 in micro accuracy and
sixth in macro accuracy},
added-at = {2012-10-09T14:04:33.000+0200},
author = {Balahur, Alexandra and Montoyo, Andrés},
biburl = {https://www.bibsonomy.org/bibtex/2ef9b8391cac9a9776286214f453c883e/bsc},
interhash = {e32cdfbc0d4e279aa4a835c533192ea5},
intrahash = {ef9b8391cac9a9776286214f453c883e},
keywords = {disambiguation mining opinion sense word},
timestamp = {2012-10-09T14:04:33.000+0200},
title = {OpAL: Applying Opinion Mining Techniques for the Disambiguation of
Sentiment Ambiguous Adjectives in SemEval-2 Task 18},
url = {http://aclweb.org/anthology-new/S/S10/S10-1099.pdf},
year = 2010
}