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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

A Knowledge-Based Approach for Aspect-Based Opinion Mining

verfasst von : Marco Federici, Mauro Dragoni

Erschienen in: Semantic Web Challenges

Verlag: Springer International Publishing

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Abstract

In the last decade, the focus of the Opinion Mining field moved to detection of the pairs “aspect-polarity” instead of limiting approaches in the computation of the general polarity of a text. In this work, we propose an aspect-based opinion mining system based on the use of semantic resources for the extraction of the aspects from a text and for the computation of their polarities. The proposed system participated at the third edition of the Semantic Sentiment Analysis (SSA) challenge took place during ESWC 2016 achieving the runner-up place in the Task #2 concerning the aspect-based sentiment analysis. Moreover, a further evaluation performed on the SemEval 2015 benchmarks demonstrated the feasibility of the proposed approach.

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Vorheriges Kapitel The IRMUDOSA System at ESWC-2016 Challenge on Semantic Sentiment Analysis
Nächstes Kapitel Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Fußnoten
Literatur
1.
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, Philadelphia, pp. 79–86. Association for Computational Linguistics, July 2002
2.
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
3.
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRef
4.
Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 415–463. Springer, Berlin (2012)CrossRef
5.
Dragoni, M.: SHELLFBK: an information retrieval-based system for multi-domain sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval 2015, Denver, Colorado, pp. 502–509. Association for Computational Linguistics, June 2015
6.
Petrucci, G., Dragoni, M.: An information retrieval-based system for multi-domain sentiment analysis. In: Gandon, F., et al. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 234–243. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25518-7_​20 CrossRef
7.
Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP, pp. 440–448 (2006)
8.
Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)MathSciNetCrossRef
9.
Dragoni, M., Tettamanzi, A.G., da Costa Pereira, C.: Propagating and aggregating fuzzy polarities for concept-level sentiment analysis. Cogn. Comput. 7(2), 186–197 (2015)CrossRef
10.
Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Heidelberg (2014)
11.
da Costa Pereira, C., Dragoni, M., Pasi, G.: A prioritized “and” aggregation operator for multidimensional relevance assessment. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 72–81. Springer, Heidelberg (2009)CrossRef
12.
Palmero Aprosio, A., Corcoglioniti, F., Dragoni, M., Rospocher, M.: Supervised opinion frames detection with RAID. In: Gandon, F., et al. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 251–263. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25518-7_​22 CrossRef
13.
Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP, pp. 1035–1045 (2010)
14.
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)
15.
Freitag, D., McCallum, A.: Information extraction with HMM structures learned by stochastic optimization. In: AAAI/IAAI, pp. 584–589 (2000)
16.
Jin, W., Ho, H.H.: A novel lexicalized HMM-based learning framework for web opinion mining. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 465–472. ACM, New York (2009)
17.
Jin, W., Ho, H.H., Srihari, R.K.: OpinionMiner: a novel machine learning system for web opinion mining and extraction. In: KDD, pp. 1195–1204 (2009)
18.
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)
19.
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: EMNLP, pp. 1533–1541 (2009)
20.
Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: WWW, pp. 959–968 (2008)
21.
Dragoni, M., Azzini, A., Tettamanzi, A.G.B.: A novel similarity-based crossover for artificial neural network evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 344–353. Springer, Heidelberg (2010)
22.
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI, pp. 1199–1204 (2009)
23.
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)CrossRef
24.
Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. SpringerBriefs in Cognitive Computation, vol. 2. Springer, Dordrecht (2012)
25.
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp. 187–205 (2007)
26.
Pan, S.J., Ni, X., Sun, J.T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: WWW, pp. 751–760 (2010)
27.
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)CrossRef
28.
Yoshida, Y., Hirao, T., Iwata, T., Nagata, M., Matsumoto, Y.: Transfer learning for multiple-domain sentiment analysis–identifying domain dependent/independent word polarity. In: AAAI, pp. 1286–1291 (2011)
29.
Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: RANLP, pp. 571–578 (2013)
30.
Huang, S., Niu, Z., Shi, C.: Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl.-Based Syst. 56, 191–200 (2014)CrossRef
31.
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)MATH
32.
Kipfer, B.A.: Roget’s 21st Century Thesaurus, 3rd edn. (2005)
33.
Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge (2010)
34.
Stone, P.J., Dunphy, D., Marshall, S.: The General Inquirer: A Computer Approach to Content Analysis. M.I.T. Press, Oxford (1966)
35.
Dragoni, M., Tettamanzi, A., da Costa Pereira, C.: Dranziera: an evaluation protocol for multi-domain opinion mining. In: Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France. European Language Resources Association (ELRA), May 2016
Metadaten
Titel
A Knowledge-Based Approach for Aspect-Based Opinion Mining
verfasst von
Marco Federici
Mauro Dragoni
Copyright-Jahr
2016
Buch
Semantic Web Challenges

Print ISBN: 978-3-319-46564-7

Electronic ISBN: 978-3-319-46565-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46565-4_11

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