Summary of the paper
| Title | United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods |
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| Authors | Vassiliki Rentoumi, Stefanos Petrakis, Manfred Klenner, George A. Vouros and 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. |
| Topics | Word Sense Disambiguation, Statistical and machine learning methods, Other |
Full paper |
United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods |
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| Bibtex | @InProceedings{RENTOUMI10.41, author = {Vassiliki Rentoumi and Stefanos Petrakis and Manfred Klenner and George A. Vouros and Vangelis Karkaletsis}, title = {United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |