@inproceedings{rentoumi-etal-2010-united,
title = "United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods",
author = "Rentoumi, Vassiliki and
Petrakis, Stefanos and
Klenner, Manfred and
Vouros, George A. and
Karkaletsis, Vangelis",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/41_Paper.pdf",
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.",
}
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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
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%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)
ACL