Abstract
An emerging field within Sentiment Analysis concerns the investigation about how sentiment concepts have to be adapted with respect to the different domains in which they are used. In the context of the Concept-Level Sentiment Analysis Challenge, we presented a system whose aims are twofold: (i) the implementation of a learning approach able to model fuzzy functions used for building the relationships graph representing the appropriateness between sentiment concepts and different domains (Task 1); and (ii) the development of a semantic resource based on the connection between an extended version of WordNet, SenticNet, and ConceptNet, that has been used both for extracting concepts (Task 2) and for classifying sentences within specific domains (Task 3).
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Notes
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Detailed results and tool demo are available at http://dkmtools.fbk.eu/moki/demo/mdfsa/mdfsa_demo.html.
References
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, Association for Computational Linguistics, pp. 79–86 (July 2002)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Carroll, J.A., van den Bosch, A., Zaenen, A. (eds.) ACL, The Association for Computational Linguistics (2007)
Bollegala, D., Weir, D.J., Carroll, J.A.: Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans. Knowl. Data Eng. 25(8), 1719–1731 (2013)
Xia, R., Zong, C., Hu, X., Cambria, E.: Feature ensemble plus sample selection: domain adaptation for sentiment classification. IEEE Int. Syst. 28(3), 10–18 (2013)
Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: Angelova, G., Bontcheva, K., Mitkov, R. (eds.) RANLP, RANLP 2011 Organising Committee/ACL, pp. 571–578 (2013)
Tsai, A.C.R., Wu, C.E., Tsai, R.T.H., Hsu, J.Y.: Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Int. Syst. 28(2), 22–30 (2013)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Kipfer, B.A.: Roget’s 21st Century Thesaurus, vol. 3. Random House, New York (2005)
Cambria, E., Speer, R., Havasi, C., Hussain, A.: Senticnet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge. Volume FS-10-02 of AAAI Technical report, AAAI (2010)
Liu, H., Singh, P.: ConceptNet: a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci. 8(3), 199–249 (1975)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM TIST 2(3), 27 (2011)
McCallum, A.K.: Mallet: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu
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Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C. (2014). A Fuzzy System for Concept-Level Sentiment Analysis. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_2
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