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The NeuroSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

  • Mauro DragoniEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 927)

Abstract

Multi-domain sentiment analysis consists in estimating the polarity of a given text by exploiting domain-specific information. One of the main issues common to the approaches discussed in the literature is their poor capabilities of being applied on domains which are different from those used for building the opinion model. In this paper, we will present an approach exploiting the linguistic overlap between domains to build sentiment models supporting polarity inference for documents belonging to every domain. Word embeddings together with a deep learning architecture have been implemented for enabling the building of multi-domain sentiment model. The proposed technique is validated by following the Dranziera protocol in order to ease the repeatability of the experiments and the comparison of the results. The outcomes demonstrate the effectiveness of the proposed approach and also set a plausible starting point for future work.

References

  1. 1.
    Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research [review article]. IEEE Comp. Int. Mag. 9(2), 48–57 (2014)CrossRefGoogle Scholar
  2. 2.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of EMNLP, Philadelphia, Association for Computational Linguistics, pp. 79–86 (July 2002)Google Scholar
  3. 3.
    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 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June 23–30, 2007, Prague, Czech Republic. The Association for Computational Linguistics (2007)Google Scholar
  4. 4.
    Pan, S.J., Ni, X., Sun, J.T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26–30, 2010, pp. 751–760. ACM (2010)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Scott, D., Daelemans, W., Walker, M.A. (eds.) Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 21–26 July, 2004, Barcelona, Spain., pp. 271–278. ACL (2004)Google Scholar
  7. 7.
    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
  8. 8.
    Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: ACL, pp. 1386–1395 (2010)Google Scholar
  9. 9.
    Tan, S., Wang, Y., Cheng, X.: Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In: Myaeng, S., Oard, D.W., Sebastiani, F., Chua, T., Leong, M. (eds.) Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, Singapore, July 20–24, 2008, pp. 743–744. ACM (2008)Google Scholar
  10. 10.
    Qiu, L., Zhang, W., Hu, C., Zhao, K.: SELC: a self-supervised model for sentiment classification. In: Cheung, D.W., Song, I., Chu, W.W., Hu, X., Lin, J.J. (eds.) Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2–6, 2009, pp. 929–936. ACM (2009)Google Scholar
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Somasundaran, S.: Discourse-level relations for Opinion Analysis. Ph.D. thesis, University of Pittsburgh (2010)Google Scholar
  14. 14.
    Wang, H., Zhou, G.: Topic-driven multi-document summarization. In: IALP, pp. 195–198 (2010)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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).  https://doi.org/10.1007/978-3-319-25518-7_20CrossRefGoogle Scholar
  17. 17.
    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).  https://doi.org/10.1007/978-3-319-46565-4_9CrossRefGoogle Scholar
  18. 18.
    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).  https://doi.org/10.1007/978-3-319-46565-4_11CrossRefGoogle Scholar
  19. 19.
    Rexha, A., Kröll, M., Dragoni, M., Kern, R.: Opinion mining with a clause-based approach. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 166–175. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69146-6_15CrossRefGoogle Scholar
  20. 20.
    Federici, M., Dragoni, M.: Aspect-based opinion mining using knowledge bases. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 133–147. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69146-6_13CrossRefGoogle Scholar
  21. 21.
    Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: Propagating and aggregating fuzzy polarities for concept-level sentiment analysis. Cogn. Comput. 7(2), 186–197 (2015)CrossRefGoogle Scholar
  22. 22.
    Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Di Iorio, A., Di Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12024-9_2CrossRefGoogle Scholar
  23. 23.
    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).  https://doi.org/10.1007/978-3-319-46565-4_10CrossRefGoogle Scholar
  24. 24.
    Dragoni, M., Petrucci, G.: A fuzzy-based strategy for multi-domain sentiment analysis. Int. J. Approx. Reason. 93, 59–73 (2018)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Petrucci, G., Dragoni, M.: The IRMUDOSA system at ESWC-2017 challenge on semantic sentiment analysis. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 148–165. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69146-6_14CrossRefGoogle Scholar
  26. 26.
    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 (LNAI), vol. 5883, pp. 72–81. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10291-2_8CrossRefGoogle Scholar
  27. 27.
    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 2th 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, Greece, May 29, 2016. Volume 1613 of CEUR Workshop Proceedings (2016). www.ceur-ws.org
  28. 28.
    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 at ESWC on Emotions, Modality, Sentiment Analysis and the Semantic Web co-located with 14th ESWC 2017, Portroz, Slovenia, May 28, 2017. Volume 1874 of CEUR Workshop Proceedings (2017). www.ceur-ws.org
  29. 29.
    Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: Jurafsky, D., Gaussier, É. (eds.) EMNLP 2007, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, 22–23 July 2006, Sydney, Australia, pp. 440–448. ACL (2006)Google Scholar
  30. 30.
    Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)MathSciNetCrossRefGoogle Scholar
  31. 31.
    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).  https://doi.org/10.1007/978-3-319-25518-7_22CrossRefGoogle Scholar
  32. 32.
    Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: COLING 2000, 18th International Conference on Computational Linguistics, Proceedings of the Conference, 2 Volumes, July 31 - August 4, 2000, Universität des Saarlandes, Saarbrücken, Germany, pp. 299–305. Morgan Kaufmann (2000)Google Scholar
  33. 33.
    Kim, S., Hovy, E.H.: Crystal: Analyzing predictive opinions on the web. In: Eisner, J. (ed.) EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28–30, 2007, Prague, Czech Republic, pp. 1056–1064. ACL (2007)Google Scholar
  34. 34.
    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).  https://doi.org/10.1007/978-3-319-47602-5_40CrossRefGoogle Scholar
  35. 35.
    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, Slovenia, May 28, 2017. Volume 1874 of CEUR Workshop Proceedings (2017). www.ceur-ws.org
  36. 36.
    Recupero, D.R., Dragoni, M., Presutti, V.: ESWC 15 challenge on concept-level sentiment analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 211–222. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25518-7_18CrossRefGoogle Scholar
  37. 37.
    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).  https://doi.org/10.1007/978-3-319-46565-4_6CrossRefGoogle Scholar
  38. 38.
    Dragoni, M., Solanki, M., Blomqvist, E. (eds.): SemWebEval 2017. CCIS, vol. 769. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69146-6CrossRefGoogle Scholar
  39. 39.
    Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9–11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1035–1045. ACL (2010)Google Scholar
  40. 40.
    Jin, W., Ho, H.H., Srihari, R.K.: OpinionMiner: a novel machine learning system for web opinion mining and extraction. In: IV, J.F.E., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.) Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pp. 1195–1204. ACM (2009)Google Scholar
  41. 41.
    Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Ellis, A., Hagino, T. (eds.) Proceedings of the 14th International Conference on World Wide Web, WWW 2005, Chiba, Japan, May 10–14, 2005, pp. 342–351. ACM (2005)Google Scholar
  42. 42.
    Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 6–7 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1533–1541. ACL (2009)Google Scholar
  43. 43.
    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: Huai, J., Chen, R., Hon, H., Liu, Y., Ma, W., Tomkins, A., Zhang, X. (eds.) Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, April 21–25, 2008, pp. 959–968. ACM (2008)Google Scholar
  44. 44.
    Dragoni, M.: NEUROSENT-PDI at semeval-2018 task 1: Leveraging a multi-domain sentiment model for inferring polarity in micro-blog text. In: Apidianaki, M., Mohammad, S.M., May, J., Shutova, E., Bethard, S., Carpuat, M. (eds.) Proceedings of The 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, June 5–6, 2018, pp. 102–108. Association for Computational Linguistics (2018)Google Scholar
  45. 45.
    Dragoni, M.: NEUROSENT-PDI at semeval-2018 task 3: understanding irony in social networks through a multi-domain sentiment model. In: Apidianaki, M., Mohammad, S.M., May, J., Shutova, E., Bethard, S., Carpuat, M. (eds.) Proceedings of The 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, June 5–6, 2018, pp. 512–519. Association for Computational Linguistics (2018)Google Scholar
  46. 46.
    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).  https://doi.org/10.1007/978-3-642-15844-5_35CrossRefGoogle Scholar
  47. 47.
    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
  48. 48.
    Dragoni, M., da Costa Pereira, C., Tettamanzi, A.G.B., Villata, S.: Combining argumentation and aspect-based opinion mining: the smack system. AI Commun. 31(1), 75–95 (2018)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Dragoni, M.: A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell. Syst. 32(3), 21–27 (2017)CrossRefGoogle Scholar
  50. 50.
    Dragoni, M., Petrucci, G.: A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans. Affect. Comput. 8(4), 457–470 (2017)CrossRefGoogle Scholar
  51. 51.
    Dragoni, M.: Computational advertising in social networks: an opinion mining-based approach. In: Haddad, H.M., Wainwright, R.L., Chbeir, R. (eds.) Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, April 09–13, 2018, pp. 1798–1804. ACM (2018)Google Scholar
  52. 52.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Huang, C., Jurafsky, D. (eds.) COLING 2010, 23rd International Conference on Computational Linguistics, Posters Volume, 23–27 August 2010, Beijing, China, pp. 36–44. Chinese Information Processing Society of China (August 2010)Google Scholar
  53. 53.
    Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: CIKM, pp. 1833–1836. (2010)Google Scholar
  54. 54.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Standford University (2009)Google Scholar
  55. 55.
    Cambria, E., Hussain, A.: Sentic computing: a common-sense-based framework for concept-level sentiment analysis (2015)CrossRefGoogle Scholar
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: Angelova, G., Bontcheva, K., Mitkov, R. (eds.) Recent Advances in Natural Language Processing, RANLP 2013, 9–11 September, 2013, Hissar, Bulgaria, pp. 571–578. RANLP 2013 Organising Committee/ACL (2013)Google Scholar
  60. 60.
    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
  61. 61.
    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, NY, USA, 9–15 July 2016, pp. 4242–4243. IJCAI/AAAI Press (2016)Google Scholar
  62. 62.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  63. 63.
    Hochreiter, S.: Untersuchungen zu dynamischen neuronalen netzen. Diploma , Technische Universität München(1991)Google Scholar
  64. 64.
    McAuley, J.J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Yang, Q., King, I., Li, Q., Pu, P., Karypis, G. (eds.) Seventh ACM Conference on Recommender Systems, RecSys ’13, Hong Kong, China, October 12–16, 2013, pp. 165–172. ACM (2013)Google Scholar
  65. 65.
    Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: DRANZIERA: an evaluation protocol for multi-domain opinion mining. In: Calzolari, N., et al. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, European Language Resources Association (ELRA) (May 2016)Google Scholar
  66. 66.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM TIST 2(3), 27:1–27:27 (2011)Google Scholar
  67. 67.
    McCallum, A.K.: Mallet: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu
  68. 68.
    Chaturvedi, I., Cambria, E., Vilares, D.: Lyapunov filtering of objectivity for spanish sentiment model. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, July 24–29, 2016, pp. 4474–4481. IEEE (2016)Google Scholar

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Authors and Affiliations

  1. 1.Fondazione Bruno KesslerTrentoItaly

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