United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods

Vassiliki Rentoumi, Stefanos Petrakis, Manfred Klenner, George A. Vouros, Vangelis Karkaletsis


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.
Anthology ID:
L10-1020
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/41_Paper.pdf
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http://www.lrec-conf.org/proceedings/lrec2010/pdf/41_Paper.pdf