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        <title>United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods</title>
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        <namePart type="given">Vassiliki</namePart>
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        <namePart type="given">Stefanos</namePart>
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        <namePart type="given">Manfred</namePart>
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    <name type="personal">
        <namePart type="given">George</namePart>
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        <namePart type="family">Vouros</namePart>
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    <name type="personal">
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    <abstract>In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction.</abstract>
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%0 Conference Proceedings
%T United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods
%A Rentoumi, Vassiliki
%A Petrakis, Stefanos
%A Klenner, Manfred
%A Vouros, George A.
%A Karkaletsis, Vangelis
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 may
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F rentoumi-etal-2010-united
%X In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/41_Paper.pdf

Markdown (Informal)

[United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods](http://www.lrec-conf.org/proceedings/lrec2010/pdf/41_Paper.pdf) (Rentoumi et al., LREC 2010)

• United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods (Rentoumi et al., LREC 2010)

ACL

• Vassiliki Rentoumi, Stefanos Petrakis, Manfred Klenner, George A. Vouros, and Vangelis Karkaletsis. 2010. United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).