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MOCHA2017: The Mighty Storage Challenge at ESWC 2017 Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

The IRMUDOSA System at ESWC-2017 Challenge on Semantic Sentiment Analysis

verfasst von : Giulio Petrucci, Mauro Dragoni

Erschienen in: Semantic Web Challenges

Verlag: Springer International Publishing

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Abstract

Multi-Domain opinion mining consists in estimating the polarity of a document by exploiting domain-specific information. One of the main issue of the approaches discussed in literature is their poor capability of being applied on domains that have not been used for building the opinion model. In this paper, we present an approach exploiting the linguistic overlap between domains for building models enabling the estimation of polarities for documents belonging to any other domain. The system implementing such an approach has been presented at the third edition of the Semantic Sentiment Analysis Challenge co-located with ESWC 2017. Fuzzy representation of features polarity supports the modeling of information uncertainty learned from training set and integrated with knowledge extracted from two well-known resources used in the opinion mining field, namely Sentic.Net and the General Inquirer. The proposed technique has been validated on a multi-domain dataset and the results demonstrated the effectiveness of the proposed approach by setting a plausible starting point for future work.

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Vorheriges Kapitel Aspect-Based Opinion Mining Using Knowledge Bases
Nächstes Kapitel Opinion Mining with a Clause-Based Approach
Fußnoten
4
All the material used for the evaluation and the built models are available at http://​goo.​gl/​pj0nWS.
 
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.
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp. 187–205 (2007)
3.
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)
4.
5.
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)
6.
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: WWW, pp. 519–528 (2003)
7.
Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: ACL, pp. 1386–1395 (2010)
8.
Tan, S., Wang, Y., Cheng, X.: Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In: SIGIR, pp. 743–744 (2008)
9.
Qiu, L., Zhang, W., Hu, C., Zhao, K.: SELC: a self-supervised model for sentiment classification. In: CIKM, pp. 929–936 (2009)
10.
Melville, P., Gryc, W., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD, pp. 1275–1284 (2009)
11.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRef
12.
Somasundaran, S.: Discourse-level relations for Opinion Analysis. Ph.D. thesis, University of Pittsburgh (2010)
13.
Wang, H., Zhou, G.: Topic-driven multi-document summarization. In: IALP, pp. 195–198 (2010)
14.
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, pp. 502–509. Association for Computational Linguistics, June 2015
15.
Petrucci, G., Dragoni, M.: An information retrieval-based system for multi-domain sentiment analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 234–243. Springer, Cham (2015). doi:10.​1007/​978-3-319-25518-7_​20 CrossRef
16.
Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Exploiting propositions for opinion mining. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 121–125. Springer, Cham (2016). doi:10.​1007/​978-3-319-46565-4_​9 CrossRef
17.
Federici, M., Dragoni, M.: A knowledge-based approach for aspect-based opinion mining. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 141–152. Springer, Cham (2016). doi:10.​1007/​978-3-319-46565-4_​11 CrossRef
18.
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
19.
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, Cham (2014). doi:10.​1007/​978-3-319-12024-9_​2
20.
Petrucci, G., Dragoni, M.: The IRMUDOSA system at ESWC-2016 challenge on semantic sentiment analysis. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 126–140. Springer, Cham (2016). doi:10.​1007/​978-3-319-46565-4_​10 CrossRef
21.
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). doi:10.​1007/​978-3-642-10291-2_​8 CrossRef
22.
Federici, M., Dragoni, M.: Towards unsupervised approaches for aspects extraction. In: Dragoni, M., Recupero, D.R., Denecke, K., Deng, Y., Declerck, T. (eds.) Joint Proceedings of the 2nd Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web and the 1st International Workshop on Extraction and Processing of Rich Semantics from Medical Texts Co-located with ESWC 2016, Heraklion, 29 May 2016. CEUR Workshop Proceedings, vol. 1613. CEUR-WS.org (2016)
23.
Federici, M., Dragoni, M.: A branching strategy for unsupervised aspect-based sentiment analysis. In: Dragoni, M., Recupero, D.R. (eds.) Proceedings of the 3rd International Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web Co-located with 14th ESWC 2017, Portroz, 28 May 2017. CEUR Workshop Proceedings, vol. 1874. CEUR-WS.org (2017)
24.
Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP, pp. 440–448 (2006)
25.
Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)CrossRefMathSciNet
26.
Palmero Aprosio, A., Corcoglioniti, F., Dragoni, M., Rospocher, M.: Supervised opinion frames detection with RAID. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 251–263. Springer, Cham (2015). doi:10.​1007/​978-3-319-25518-7_​22 CrossRef
27.
Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: COLING, pp. 299–305 (2000)
28.
Kim, S.M., Hovy, E.H.: Crystal: analyzing predictive opinions on the web. In: EMNLP-CoNLL, pp. 1056–1064 (2007)
29.
Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Polarity classification for target phrases in tweets: a Word2Vec approach. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 217–223. Springer, Cham (2016). doi:10.​1007/​978-3-319-47602-5_​40 CrossRef
30.
Rexha, A., Kröll, M., Kern, R., Dragoni, M.: An embedding approach for microblog polarity classification. In: Dragoni, M., Recupero, D.R. (eds.) Proceedings of the 3rd International Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web Co-located with 14th ESWC 2017, Portroz, 28 May 2017. CEUR Workshop Proceedings, vol. 1874. CEUR-WS.org (2017)
31.
Dragoni, M., Reforgiato Recupero, D.: Challenge on fine-grained sentiment analysis within ESWC2016. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 79–94. Springer, Cham (2016). doi:10.​1007/​978-3-319-46565-4_​6 CrossRef
32.
Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP, pp. 1035–1045 (2010)
33.
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)
34.
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)
35.
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: EMNLP, pp. 1533–1541 (2009)
36.
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)
37.
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 2010. LNCS, vol. 6238, pp. 344–353. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-15844-5_​35
38.
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
39.
Dragoni, M.: A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell. Syst. 32(3), 21–27 (2017)CrossRef
40.
Dragoni, M., Petrucci, G.: A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans. Affect. Comput. PP(99), 1 (2017)CrossRef
41.
Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: COLING (Posters), pp. 36–44 (2010)
42.
Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: CIKM, pp. 1833–1836 (2010)
43.
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Standford University (2009)
44.
Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham (2015)CrossRef
45.
Cambria, E., Hussain, A.: Sentic album: content-, concept-, and context-based online personal photo management system. Cogn. Comput. 4(4), 477–496 (2012)CrossRef
46.
Wang, Q.F., Cambria, E., Liu, C.L., Hussain, A.: Common sense knowledge for handwritten Chinese recognition. Cogn. Comput. 5(2), 234–242 (2013)CrossRef
47.
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)
48.
Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: RANLP, pp. 571–578 (2013)
49.
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
50.
Dragoni, M., da Costa Pereira, C., Tettamanzi, A.G.B., Villata, S.: Smack: an argumentation framework for opinion mining. In: Kambhampati, S. (ed.) Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, 9–15 July 2016, pp. 4242–4243. IJCAI/AAAI Press (2016)
51.
Cambria, E., Olsher, D., Rajagopal, D.: Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: AAAI, pp. 1515–1521 (2014)
52.
Stone, P.J., Dunphy, D., Smith, M.: The General Inquirer: A Computer Approach to Content Analysis. M.I.T Press, Oxford (1966)
53.
54.
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, pp. 55–60. Association for Computational Linguistics, June 2014
55.
van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)MATH
56.
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci. 8(3), 199–249 (1975)CrossRefMATHMathSciNet
57.
Hellendoorn, H., Thomas, C.: Defuzzification in fuzzy controllers. Intell. Fuzzy Syst. 1, 109–123 (1993)
58.
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. European Language Resources Association (ELRA), May 2016
Metadaten
Titel
The IRMUDOSA System at ESWC-2017 Challenge on Semantic Sentiment Analysis
verfasst von
Giulio Petrucci
Mauro Dragoni
Copyright-Jahr
2017
Buch
Semantic Web Challenges

Print ISBN: 978-3-319-69145-9

Electronic ISBN: 978-3-319-69146-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-69146-6_14

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