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Multilingual Subjectivity Analysis Using Machine Translation
Carmen Banea and Rada Mihalcea
University of North Texas
University of Pittsburgh
University of North Texas
Although research in other languages is in-
creasing, much of the work in subjectivity
analysis has been applied to English data,
mainly due to the large body of electronic re-
sources and tools that are available for this lan-
guage. In this paper, we propose and evalu-
ate methods that can be employed to transfer a
repository of subjectivity resources across lan-
guages. Specifically, we attempt to leverage
on the resources available for English and, by
employing machine translation, generate re-
sources for subjectivity analysis in other lan-
guages. Through comparative evaluations on
two different languages (Romanian and Span-
ish), we show that automatic translation is a
viable alternative for the construction of re-
sources and tools for subjectivity analysis in
a new target language.
We have seen a surge in interest towards the ap-
plication of automatic tools and techniques for the
extraction of opinions, emotions, and sentiments in
text (subjectivity). A large number of text process-
ing applications have already employed techniques
for automatic subjectivity analysis, including auto-
matic expressive text-to-speech synthesis (Alm et
al., 2005), text semantic analysis (Wiebe and Mihal-
cea, 2006; Esuli and Sebastiani, 2006), tracking sen-
timent timelines in on-line forums and news (Lloyd
et al., 2005; Balog et al., 2006), mining opinions
from product reviews (Hu and Liu, 2004), and ques-
tion answering (Yu and Hatzivassiloglou, 2003).
A significant fraction of the research work to date
in subjectivity analysis has been applied to English,
which led to several resources and tools available for
this language. In this paper, we explore multiple
paths that employ machine translation while lever-
aging on the resources and tools available for En-
glish, to automatically generate resources for sub-
jectivity analysis for a new target language. Through
experiments carried out with automatic translation
and cross-lingual projections of subjectivity annota-
tions, we try to answer the following questions.
First, assuming an English corpus manually an-
notated for subjectivity, can we use machine trans-
lation to generate a subjectivity-annotated corpus in
the target language? Second, assuming the availabil-
ity of a tool for automatic subjectivity analysis in
English, can we generate a corpus annotated for sub-
jectivity in the target language by using automatic
subjectivity annotations of English text and machine
translation? Finally, third, can these automatically
generated resources be used to effectively train tools
for subjectivity analysis in the target language?
Since our methods are particularly useful for lan-
guages with only a few electronic tools and re-
sources, we chose to conduct our initial experiments
on Romanian, a language with limited text process-
ing resources developed to date. Furthermore, to
validate our results, we carried a second set of ex-
periments on Spanish. Note however that our meth-
ods do not make use of any target language specific
knowledge, and thus they are applicable to any other
language as long as a machine translation engine ex-
ists between the selected language and English.
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Banea, Carmen; Mihalcea, Rada, 1974-; Wiebe, Janyce M. & Hassan, Samer. Multilingual Subjectivity Analysis Using Machine Translation, paper, October 2008; (digital.library.unt.edu/ark:/67531/metadc31007/m1/1/: accessed November 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.