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

  • Giulio Petrucci
  • Mauro DragoniEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 769)

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.

References

  1. 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 2002Google Scholar
  2. 2.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp. 187–205 (2007)Google Scholar
  3. 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)Google Scholar
  4. 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, Boston (2012). doi: 10.1007/978-1-4614-3223-4_13 CrossRefGoogle Scholar
  5. 5.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)Google Scholar
  6. 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)Google Scholar
  7. 7.
    Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: ACL, pp. 1386–1395 (2010)Google Scholar
  8. 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)Google Scholar
  9. 9.
    Qiu, L., Zhang, W., Hu, C., Zhao, K.: SELC: a self-supervised model for sentiment classification. In: CIKM, pp. 929–936 (2009)Google Scholar
  10. 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)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Somasundaran, S.: Discourse-level relations for Opinion Analysis. Ph.D. thesis, University of Pittsburgh (2010)Google Scholar
  13. 13.
    Wang, H., Zhou, G.: Topic-driven multi-document summarization. In: IALP, pp. 195–198 (2010)Google Scholar
  14. 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 2015Google Scholar
  15. 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 CrossRefGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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 Google Scholar
  20. 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 CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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)Google Scholar
  23. 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)Google Scholar
  24. 24.
    Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP, pp. 440–448 (2006)Google Scholar
  25. 25.
    Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)CrossRefMathSciNetGoogle Scholar
  26. 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 CrossRefGoogle Scholar
  27. 27.
    Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: COLING, pp. 299–305 (2000)Google Scholar
  28. 28.
    Kim, S.M., Hovy, E.H.: Crystal: analyzing predictive opinions on the web. In: EMNLP-CoNLL, pp. 1056–1064 (2007)Google Scholar
  29. 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 CrossRefGoogle Scholar
  30. 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)Google Scholar
  31. 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 CrossRefGoogle Scholar
  32. 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)Google Scholar
  33. 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)Google Scholar
  34. 34.
    Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)Google Scholar
  35. 35.
    Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: EMNLP, pp. 1533–1541 (2009)Google Scholar
  36. 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)Google Scholar
  37. 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 Google Scholar
  38. 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)CrossRefGoogle Scholar
  39. 39.
    Dragoni, M.: A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell. Syst. 32(3), 21–27 (2017)CrossRefGoogle Scholar
  40. 40.
    Dragoni, M., Petrucci, G.: A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans. Affect. Comput. PP(99), 1 (2017)CrossRefGoogle Scholar
  41. 41.
    Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: COLING (Posters), pp. 36–44 (2010)Google Scholar
  42. 42.
    Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: CIKM, pp. 1833–1836 (2010)Google Scholar
  43. 43.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Standford University (2009)Google Scholar
  44. 44.
    Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham (2015)CrossRefGoogle Scholar
  45. 45.
    Cambria, E., Hussain, A.: Sentic album: content-, concept-, and context-based online personal photo management system. Cogn. Comput. 4(4), 477–496 (2012)CrossRefGoogle Scholar
  46. 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)CrossRefGoogle Scholar
  47. 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)Google Scholar
  48. 48.
    Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: RANLP, pp. 571–578 (2013)Google Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)Google Scholar
  51. 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)Google Scholar
  52. 52.
    Stone, P.J., Dunphy, D., Smith, M.: The General Inquirer: A Computer Approach to Content Analysis. M.I.T Press, Oxford (1966)Google Scholar
  53. 53.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar
  54. 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 2014Google Scholar
  55. 55.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)zbMATHGoogle Scholar
  56. 56.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci. 8(3), 199–249 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  57. 57.
    Hellendoorn, H., Thomas, C.: Defuzzification in fuzzy controllers. Intell. Fuzzy Syst. 1, 109–123 (1993)Google Scholar
  58. 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 2016Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universitá di TrentoTrentoItaly
  2. 2.Fondazione Bruno KesslerTrentoItaly

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