Cognitive Computation

, Volume 7, Issue 2, pp 211–225 | Cite as

Sentilo: Frame-Based Sentiment Analysis

  • Diego Reforgiato RecuperoEmail author
  • Valentina Presutti
  • Sergio Consoli
  • Aldo Gangemi
  • Andrea Giovanni Nuzzolese


Sentilo is an unsupervised, domain-independent system that performs sentiment analysis by hybridizing natural language processing techniques and semantic Web technologies. Given a sentence expressing an opinion, Sentilo recognizes its holder, detects the topics and subtopics that it targets, links them to relevant situations and events referred to by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel lexical resource, which enables a proper propagation of sentiment scores from topics to subtopics, and on a formal model expressing the semantics of opinion sentences. Sentilo provides its output as a RDF graph, and whenever possible it resolves holders’ and topics’ identity on Linked Data.


Opinion mining Sentic computing Sentiment analysis Conceptual frames 



The work described in this paper was performed with the support of the PRISMA (PiattafoRme cloud Interoperabili per SMArt-government) Project, funded by the MIUR (Ministero dell’Istruzione, dell’Università e della Ricerca).


  1. 1.
    Alm CO, Roth D, Sproat R. Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of HLT/EMNLP, Vancouver, Canada, 2005. p. 347–54.Google Scholar
  2. 2.
    Baccianella A, Esuli S, Sebastiani F. SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D, editors. Proceedings of the seventh conference on international language resources and evaluation (LREC’10). Malta: Valletta; 2010.Google Scholar
  3. 3.
    Bizer C, Heath T, Berners-Lee T. Linked data: the story so far. Int J Semant Web Inf Syst. 2009;5(3):1–22.CrossRefGoogle Scholar
  4. 4.
    Bos J. Wide-coverage semantic analysis with Boxer. In: Proceedings of the 2008 conference on semantics in text processing (STEP ’08), Stroudsburg, USA, 2008. p. 277–86.Google Scholar
  5. 5.
    Brown SW, Dligach D, Palmer M. VerbNet class assignment as a WSD task. In: Proceedings of the ninth international conference on computational semantics (IWCS ’11), Stroudsburg, USA, 2011. p. 85–94.Google Scholar
  6. 6.
    Cai K, Spangler S, Chen Y, Zhang L. Leveraging sentiment analysis for topic detection. Web Intell Agent Syst. 2010;8(3):291–302.Google Scholar
  7. 7.
    Cambria E, Grassi M, Hussain A, Havasi C. Sentic computing for social media marketing. Multimed Tools Appl. 2012;59(2):557–77.CrossRefGoogle Scholar
  8. 8.
    Cambria E, Hussain A. Sentic computing: techniques, tools, and applications, vol. 2. Heidelberg: Springer; 2012.Google Scholar
  9. 9.
    Cambria E, Hussain A, Havasi C, Eckl C. Common sense computing: from the society of mind to digital intuition and beyond. In: Fierrez J, Ortega-Garcia J, Esposito A, Drygajlo A, Faundez-Zanuy M, editors. Biometric ID management and multimodal communication. Lecture notes in computer science, vol. 5707. Heidelberg: Springer; 2009. p. 252–9.CrossRefGoogle Scholar
  10. 10.
    Cambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intell Syst. 2013;28(2):15–21.CrossRefGoogle Scholar
  11. 11.
    Cambria E, Song Y, Wang H, Hussain A. Isanette: a common and common sense knowledge base for opinion mining. In: Spiliopoulou M, Wang H, Cook D, Pei J, Wang W, Zaiane O, Wu X, editors. Proceedings of the IEEE international conference on data mining (ICDM), 2011; p. 315–22.Google Scholar
  12. 12.
    Cambria E, Olsher D, Rajagopal D. Senticnet 3: a common and common-sense knowledge base for cognition–driven sentiment analysis. In: Brodley CE, Stone P, editors. Twenty-eight AAAI conference on artificial intelligence. Palo Alto, CA: AAAI Press; 2014.Google Scholar
  13. 13.
    Chen H, Wuand Z, Cudré-Mauroux P. Semantic web meets computational intelligence: state of the art and perspectives. IEEE Comput Intell Mag. 2012;7(2):67–74.CrossRefGoogle Scholar
  14. 14.
    Das D, Bandyopadhyay S. Sentence-level emotion and valence tagging. Cognit Comput. 2012;4(4):420–35.CrossRefGoogle Scholar
  15. 15.
    Elliott CD. The affective reasoner: a process model of emotions in a multi-agent system. PhD thesis, Northwestern University, Evanston, USA, 1992. UMI O. No. GAX92-29901.Google Scholar
  16. 16.
    Fellbaum C. WordNet: an electronic lexical database. Cambridge, MA: The MIT Press; 1998.Google Scholar
  17. 17.
    Gangemi A, Presutti V. Towards a pattern science for the semantic web. Semant. Web. 2010;1(1,2):61–8.Google Scholar
  18. 18.
    Gangemi A, Presutti V, Reforgiato Recupero D. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput Intell Mag. 2014;9(1):20–30. Google Scholar
  19. 19.
    Goertzel B, Silverman K, Hartley C, Bugaj S, Ross M. The baby webmind project. In: Proceedings of The annual conference of the society for the study of artificial intelligence and the simulation of behaviour (AISB); 2000. p. 147–48Google Scholar
  20. 20.
    Hu M, Liu B. Mining opinion features in customer reviews. In: Proceedings of the 19th national conference on artificial intelligence (AAAI’04); 2004. p. 755–60Google Scholar
  21. 21.
    Johansson R, Moschitti A. Relational features in fine-grained opinion analysis. Comput Linguist. 2013;39(3):473–509.CrossRefGoogle Scholar
  22. 22.
    Kamp H. A theory of truth and semantic representation. In: Groenendijk JAG, Janssen TMV, Stokhof MBJ, editors. Formal methods in the study of language, vol. 1. Amsterdam, NE: Mathematisch Centrum; 1981. p. 277–322.Google Scholar
  23. 23.
    Kazemzadeh A, Lee S, Narayanan SS. Fuzzy logic models for the meaning of emotion words. IEEE Comput Intell Mag. 2013;8(2):34–49.CrossRefGoogle Scholar
  24. 24.
    Lau R, Xia Y, Ye Y. A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput Intell Mag. 2014;9(1):31–43.CrossRefGoogle Scholar
  25. 25.
    Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, et al. DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web J. 2014.Google Scholar
  26. 26.
    Levin B. English verb classes and alternations a preliminary investigation. Chicago: University of Chicago Press; 1993.Google Scholar
  27. 27.
    Lin C, He Y, Everson R, Ruger S. Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng. 2012;24(6):1134–45.CrossRefGoogle Scholar
  28. 28.
    Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. Morgan&Claypool Publishers; 2012.Google Scholar
  29. 29.
    Nicolov N, Salvetti F, Martin J, Liberman M. Computational approaches to analysing weblogs: papers from 2006 AAAI spring symposium. Menlo Park, USA: AAAI Press; 2006.Google Scholar
  30. 30.
    Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics (ACL ’04), Barcelona, Spain, 2004.Google Scholar
  31. 31.
    Pang B, Lee L. Opinion mining and sentiment analysis, volume 2 of Foundations and trends in information retrieval. now Publishers Inc., Delft, Netherlands, 2008.Google Scholar
  32. 32.
    Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing (EMNLP ’02), vol. 10, Stroudsburg, USA, 2002. p. 79–86.Google Scholar
  33. 33.
    Presutti V, Draicchio F, Gangemi A. Knowledge extraction based on discourse representation theory and linguistic frames. Knowledge Engineering and Knowledge Management, volume 7603 of Lecture Notes in Computer Science. Heidelberg, DE: Springer; 2012. p. 114–29.Google Scholar
  34. 34.
    Saif H, He Y, Alani H.: Semantic sentiment analysis of twitter. In: Proceedings of the 11th international conference on the semantic web (ISWC’12), volume Part I. Boston, MA: Springer; 2012. p. 508–24Google Scholar
  35. 35.
    Semantic Engines LLC. Opinion crawl., 2010.
  36. 36.
    Sentiment 140., 2013.
  37. 37.
    Socher R, Perelygin A, Wu J, Chuang J, Manning C, Ng A, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Conference on empirical methods in natural language processing (EMNLP 2013), 2013.Google Scholar
  38. 38.
    Social mention., 2011.
  39. 39.
    Somasundaran S, Wiebe J, Ruppenhofer J. Discourse level opinion interpretation. In: Proceedings of the 22nd international conference on computational linguistics (COLING ’08), vol. 11, Manchester, UK; 2008. p. 801–8.Google Scholar
  40. 40.
    Sood SO, Owsley S, Hammond KJ, Birnbaum L. Reasoning through search: a novel approach to sentiment classification. In: Proceedings of the 16th international world wide web (WWW) conference, Banff, Canada, 2007.Google Scholar
  41. 41.
    Sood SO, Vasserman L. ESSE: exploring mood on the web. In: Proceedings of the international conference on weblogs and social media (ICWSM), Seattle, USA, 2009.Google Scholar
  42. 42.
    Tam DN. Computation in emotional processing: quantitative confirmation of proportionality hypothesis for angry unhappy emotional intensity to perceived loss. Cognit Comput. 2011;3(2):394–415.CrossRefGoogle Scholar
  43. 43.
    Titov I, McDonald R. Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th international world wide web (WWW) conference, Beijing, China, 2008. p. 111–20.Google Scholar
  44. 44.
    Turney PD. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics (ACL ’02), Philadelphia, PA, 2002. p. 417–24.Google Scholar
  45. 45.
    University of Stanford. Stanford Sentiment Analysis., 2014.
  46. 46.
    Wiebe J, Wilson T, Cardie C. Annotating expressions of opinions and emotions in language. Lang Resour Eval. 2005;39(2–3):165–210.CrossRefGoogle Scholar
  47. 47.
    Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing (HLT ’05), Vancouver, Canada, 2005. p. 347–54Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Diego Reforgiato Recupero
    • 1
    Email author
  • Valentina Presutti
    • 2
  • Sergio Consoli
    • 1
  • Aldo Gangemi
    • 2
    • 3
  • Andrea Giovanni Nuzzolese
    • 2
    • 4
  1. 1.Semantic Technology Laboratory, National Research Council (CNR)Institute of Cognitive Sciences and TechnologiesCataniaItaly
  2. 2.Semantic Technology Laboratory, National Research Council (CNR)Institute of Cognitive Sciences and TechnologiesRomeItaly
  3. 3.LIPN, Sorbone Cité, UMR CNRSUniversity Paris 13ParisFrance
  4. 4.Department of Computer Science and EngineeringUniversity of BolgonaBolognaItaly

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