Challenge on Fine-Grained Sentiment Analysis Within ESWC2016

  • Mauro Dragoni
  • Diego Reforgiato RecuperoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)


The wide spread of the social media has given users a means to express and share their opinions and thoughts on a large range of topics and events. The number of opinions, emotions, sentiments that are being expressed within social media grows at an exponential rate; all these data can be exploited in order to come up with useful insights, analytics, etc. Initial Sentiment Analysis systems used lexical and statistical resources to automatically assess polarities of opinions and sentiment. With the raise of the Semantic Web, it has been proved that Sentiment Analysis techniques can have higher performances if they use semantic features. This generated further opportunities for the research domain as well as the market domain where key stakeholders need to catch up with the latest technology if they want to be compelling. Therefore, deep understanding of natural language text and the related semantics are urgent matter to be familiar with. Following the first two editions, the third edition of the Fine-Grained Sentiment Analysis challenge aims at providing a stimulus toward this direction. On the one hand, it represents an event where researchers can learn and share their methods and how they employed Semantics for Sentiment Analysis. On the other hand, it offers an occasion for stakeholders to get an idea of what research is being developed and where the research is headed to plan future strategies within the domain of Sentiment Analysis.



Challenge Organizers want to thank Springer for supporting the provided awards also for this year edition. Moreover, the research leading to these results has received funding from the European Union Horizons 2020 the Framework Programme for Research and Innovation (2014–2020) under grant agreement 643808 Project MARIO Managing active and healthy aging with use of caring service robots.


  1. 1.
    Subrahmanian, V.S., Reforgiato, D.: AVA: adjective-verb-adverb combinations for sentiment analysis. IEEE Intell. Syst. 23, 43–50 (2008)CrossRefGoogle Scholar
  2. 2.
    Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Subrahmanian, V.S.: Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Short paper (2007)Google Scholar
  3. 3.
    Gan tzandetal, J.: The expanding digital universe: a forecast of world wide information growth through, 2007 (2010)Google Scholar
  4. 4.
    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. Revised Selected PapersCrossRefGoogle Scholar
  5. 5.
    Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.: Sentilo: frame-based sentiment analysis. Cogn. Comput. 7(2), 211–225 (2014)Google Scholar
  6. 6.
    Consoli, S., Gangemi, A., Nuzzolese, A.G., Reforgiato Recupero, D., Spampinato, D.: Extraction of topics-events semantic relationships for opinion propagation in sentiment analysis. In: Proceedings of Extended Semantic Web Conference (ESWC), Crete, GR (2014)Google Scholar
  7. 7.
    Gangemi, A., Presutti, V., Reforgiato Recupero, D.: Frame-based detection of opinion holders, topics: a model and a tool. IEEE Comput. Intell. Mag. 9(1), 20–30 (2014)CrossRefGoogle Scholar
  8. 8.
    Dragoni, M., Tettamanzi, A.G.B., Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., et al. (eds.) Semantic Web Evaluation Challenge. CCIS, vol. 475, pp. 21–27. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Recupero, D.R., Cambria, E.: ESWC 2014 challenge: concept-level sentiment analysis. SemWebEval@ESWC 2014, pp. 3–20, May 2014.
  10. 10.
    Recupero, D.R., Dragoni, M., Presutti, V.: ESWC15 challenge on concept-level sentiment analysis. SemWebEval@ESWC (2011) Observation of Strains, pp. 211–222, May 2015Google Scholar
  11. 11.
    Presutti, V., et al. (eds.): Semantic Web Evaluation Challenge. CCIS, vol. 475. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A.: Semantic Web Evaluation Challenges. Second SemWebEval Challenge at ESWC, Portoroz, Slovenia, May 31-June 4, Revised Selected Papers. Springer (2015)Google Scholar
  13. 13.
    Gangemi, A., Alani, H., Nissim, M., Cambria, E., Recupero, D.R., Lanfranchi, V., Kauppinen, T.: Joint Proceedings of the 1th Workshop on Semantic Sentiment Analysis (SSA2014), and the Workshop on Social Media and Linked Data for Emergency Response (SMILE 2014), Co-located with 11th European Semantic Web Conference (ESWC 2014), 25 May 2014, Crete, Greece (2014).
  14. 14.
    Cambria, E., Olsher, D., Rajagopal, D.: Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Brodley, C.E., Stone, P. (eds.) Twenty-Eight AAAI Conference on Artificial Intelligence, pp. 1515–1521. AAAI Press, Palo Alto, July 2014Google 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, pp. 502–509. Association for Computational Linguistics, Denver, June 2015Google Scholar
  16. 16.
    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
  17. 17.
    Dragoni, M., Tettamanzi, A., Pereira, C.D.C.: DRANZIERA: an evaluation protocol for multi-domain opinion mining. In: Calzolari, N. (Conference Chair), 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 2016Google Scholar
  18. 18.
    Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Rafael (2012)Google Scholar
  19. 19.
    Aprosio, A.P., 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. Revised Selected PapersCrossRefGoogle Scholar
  20. 20.
    Rosa, E.D., Durante, A.: App2check extension for sentiment analysis of amazon products reviews. In: Sack et al. [26], pp. 95–107Google Scholar
  21. 21.
    Federici, M., Dragoni, M.: A knowledge-based approach for aspect-based opinion mining. In: Sack et al. [26], pp. 141–152Google Scholar
  22. 22.
    Sygkounas, E., Li, X., Rizzo, G., Troncy, R.: Sentiment polarity detection from amazon reviews: an experimental study. In: Sack et al. [26], pp. 108–120Google Scholar
  23. 23.
    Jebbara, S., Cimiano, P.: Aspect-based sentiment analysis using a two-step neural network architectures. In: Sack et al. [26], pp. 153–167Google Scholar
  24. 24.
    Rexha, A., Kröll, M., Dragoni, M., Kerns, R.: Exploiting propositions for opinion mining. In: Sack et al. [26], pp. 121–125Google Scholar
  25. 25.
    Petrucci, G., Dragoni, M.: The IRMUDOSA system at ESWC challenge on semantic sentiment analysis. In: Sack et al. [26], pp. 126–140Google Scholar
  26. 26.
    Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.): SemWebEval 2016. CCIS, vol. 641. Springer, Heidelberg (2016)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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