Summary of the paper
| Title | United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods |
|---|---|
| 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 sentimentanalysis. In the course of those experiments, we observed that our machinelearning method, although able to cope well with figurative language could notalways reach a certain decision about the polarity orientation of sentences,yielding erroneous evaluations. We support the conjecture that these casesbearing mild figurativeness could be better handled by a rule-based system.These two systems, acting complementarily, could bridge the gap between machinelearning and rule-based approaches. Experimental results using the corpus ofthe Affective Text Task of SemEval ’07, provide evidence in favor of thisdirection. |
| Language | Other |
| 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 |
| Bibtex | @InProceedings{RENTOUMI10.41, author = {Vassiliki Rentoumi, Stefanos Petrakis, Manfred Klenner, 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 conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |