Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Semantic Web Challenges (SemWebEval 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 927))

Included in the following conference series:

  • 323 Accesses

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 2018 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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval082.pdf.

  2. 2.

    https://wordnet.princeton.edu/.

  3. 3.

    http://sentic.net/.

  4. 4.

    http://www.wjh.harvard.edu/~inquirer/spreadsheet_guide.htm.

  5. 5.

    https://wordnet.princeton.edu/.

  6. 6.

    The used stopwords list is available at http://www.lextek.com/manuals/onix/stopwords1.html.

  7. 7.

    http://stanfordnlp.github.io/CoreNLP/index.html.

  8. 8.

    http://www.alt.qcri.org/semeval2015/task12/.

  9. 9.

    http://alt.qcri.org/semeval2016/task5/.

References

  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)

    Google Scholar 

  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)

    Google Scholar 

  3. 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 (2012)

    Google Scholar 

  4. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)

    Google Scholar 

  5. 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 

  6. Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: ACL, pp. 1386–1395 (2010)

    Google Scholar 

  7. 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 

  8. Qiu, L., Zhang, W., Hu, C., Zhao, K.: Selc: a self-supervised model for sentiment classification. In: CIKM, pp. 929–936 (2009)

    Google Scholar 

  9. 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 

  10. Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  11. Somasundaran, S.: Discourse-level relations for Opinion Analysis. Ph.D. thesis, University of Pittsburgh (2010)

    Google Scholar 

  12. Wang, H., Zhou, G.: Topic-driven multi-document summarization. In: IALP, pp. 195–198 (2010)

    Google Scholar 

  13. 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 

  14. 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_20

    Chapter  Google Scholar 

  15. 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_9

    Chapter  Google Scholar 

  16. 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_11

    Chapter  Google Scholar 

  17. 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_15

    Chapter  Google Scholar 

  18. 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_13

    Chapter  Google Scholar 

  19. Dragoni, M., Tettamanzi, A.G., da Costa Pereira, C.: Propagating and aggregating fuzzy polarities for concept-level sentiment analysis. Cognit. Comput. 7(2), 186–197 (2015)

    Article  Google Scholar 

  20. 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_2

    Chapter  Google Scholar 

  21. 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_10

    Chapter  Google Scholar 

  22. Dragoni, M., Petrucci, G.: A fuzzy-based strategy for multi-domain sentiment analysis. Int. J. Approx. Reason. 93, 59–73 (2018)

    Article  MathSciNet  Google Scholar 

  23. 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_14

    Chapter  Google Scholar 

  24. 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: Emergent Perspectives in Artificial Intelligence, XIth International Conference of the Italian Association for Artificial Intelligence, Reggio Emilia, Italy, December 9–12, 2009, Proceedings. Volume 5883 of Lecture Notes in Computer Science, pp. 72–81. Springer (2009)

    Google Scholar 

  25. Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.): ESWC 2016. LNCS, vol. 9989. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47602-5

    Google Scholar 

  26. Federici, M., Dragoni, M.: A branching strategy for unsupervised aspect-based sentimentanalysis. In Dragoni, M., Recupero, D.R. (eds.) Proceedings of the 3rd International Workshop at ESWC on Emotions, Modality, Sentiment Analysis and the SemanticWeb co-located with 14th ESWC 2017, Portroz, Slovenia, May 28, 2017, CEUR Workshop Proceedings, vol. 1874 (2017). http://ceur-ws.org/

  27. Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP, pp. 440–448 (2006)

    Google Scholar 

  28. Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Comput. Intell. 22(2), 73–99 (2006)

    Article  MathSciNet  Google Scholar 

  29. 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_22

    Chapter  Google Scholar 

  30. Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: COLING, pp. 299–305 (2000)

    Google Scholar 

  31. Kim, S.M., Hovy, E.H.: Crystal: analyzing predictive opinions on the web. In: EMNLP-CoNLL, pp. 1056–1064 (2007)

    Google Scholar 

  32. 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_40

    Chapter  Google Scholar 

  33. Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.): ESWC 2017. LNCS, vol. 10250. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58451-5

    Book  Google Scholar 

  34. 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_18

    Chapter  Google Scholar 

  35. Dragoni, M., Reforgiato Recupero, D.: Challenge on fine-Ggrained 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_6

    Chapter  Google Scholar 

  36. Dragoni, M., Solanki, M., Blomqvist, E. (eds.): SemWebEval 2017. CCIS, vol. 769. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6

    Book  Google Scholar 

  37. 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 

  38. 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 

  39. Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)

    Google Scholar 

  40. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: EMNLP, pp. 1533–1541 (2009)

    Google Scholar 

  41. Su, Q., et al.: Hidden sentiment association in chinese web opinion mining. In: WWW, pp. 959–968 (2008)

    Google Scholar 

  42. 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, 5–6 June 2018, pp. 102–108. Association for Computational Linguistics (2018)

    Google Scholar 

  43. 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, 5–6 June 2018, pp. 512–519. Association for Computational Linguistics (2018)

    Google Scholar 

  44. 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_35

    Chapter  Google Scholar 

  45. Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)

    Article  Google Scholar 

  46. 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)

    Article  MathSciNet  Google Scholar 

  47. Dragoni, M.: A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell. Syst. 32(3), 21–27 (2017)

    Article  Google Scholar 

  48. Dragoni, M., Petrucci, G.: A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans. Affect. Comput. 8(4), 457–470 (2017)

    Article  Google Scholar 

  49. 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, 09–13 April 2018, pp. 1798–1804. ACM (2018)

    Google Scholar 

  50. Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: COLING (Posters), pp. 36–44. (2010)

    Google Scholar 

  51. Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: CIKM, pp. 1833–1836 (2010)

    Google Scholar 

  52. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Standford University (2009)

    Google Scholar 

  53. Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework For Concept-Level Sentiment Analysis (2015)

    Book  Google Scholar 

  54. Cambria, E., Hussain, A.: Sentic album: content-, concept-, and context-based online personal photo management system. Cognit. Comput. 4(4), 477–496 (2012)

    Article  Google Scholar 

  55. Wang, Q.F., Cambria, E., Liu, C.L., Hussain, A.: Common sense knowledge for handwritten chinese recognition. Cognit. Comput. 5(2), 234–242 (2013)

    Article  Google Scholar 

  56. 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 

  57. 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 

  58. Yoshida, Y., Hirao, T., Iwata, T., Nagata, M., Matsumoto, Y.: Transfer learning for multiple-domain sentiment analysis–identifying domain dependent/independent word polarity. AAA I, 1286–1291 (2011)

    Google Scholar 

  59. Ponomareva, N., Thelwall, M.: Semi-supervised vs. cross-domain graphs for sentiment analysis. In: RANLP, pp. 571–578 (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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, IJCAI/AAAI Press, pp. 4242–4243 (2016)

    Google Scholar 

  62. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  63. Kipfer, B.A.: Roget’s 21st century thesaurus, 3rd edn (2005)

    Google Scholar 

  64. Cambria, E., Speer, R., Havasi, C., Hussain, A.: Senticnet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge (2010)

    Google Scholar 

  65. P.J., S., Dunphy, D., Marshall, S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Oxford (1966)

    Google Scholar 

  66. Dragoni, M., Tettamanzi, A., da Costa Pereira, C.: Dranziera: an evaluation protocol for multi-domain opinion mining. In: Chair, N.C.C., 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 

Download references

Author information

Authors and Affiliations

  1. Universitá di Trento, Trento, Italy

    Marco Federici

  2. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

Authors
  1. Marco Federici
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauro Dragoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mauro Dragoni .

Editor information

Editors and Affiliations

  1. Laboratoire d'Informatique de Paris-Nord, Paris 13 University, Villetaneuse, France

    Davide Buscaldi

  2. University of Bologna, Bologna, Italy

    Aldo Gangemi

  3. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Federici, M., Dragoni, M. (2018). The KABSA System at ESWC-2018 Challenge on Semantic Sentiment Analysis. In: Buscaldi, D., Gangemi, A., Reforgiato Recupero, D. (eds) Semantic Web Challenges. SemWebEval 2018. Communications in Computer and Information Science, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-00072-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00072-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00071-4

  • Online ISBN: 978-3-030-00072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation