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Sentilo: Frame-Based Sentiment Analysis

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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.

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  1. EuroSentiment EU FP7 project., 2014.

  2. F1 measures.


  4. The framenet project., 2002.

  5. F1 measures.

  6. FRED,, December 2014.

  7. Dolce Ultra Lite Ontology.

  8. Prefix dul: stands for and prefix rdf: stands for; prefix fred: refers to a locally defined namespace that can be customized by users.

  9. Prefix refers to VerbNet [5].

  10. Notice that this process can be recursive, and the role of main topic/subtopic in such cases would be contextual to the current iteration.

  11. An excerpt of SentiloNet can be downloaded from


  13. Users can choose between the two by means of a selection box included in the graphical user interface of Sentilo prototype available at

  14. We also include in the table the respective sentiment scores.

  15. We omit the prefix sentilo: for the sake of readability and brevity.

  16. Apache Felix:

  17. Sentilo,

  18. Sentilo Advanced User Interface,


  20. Sentilo,


  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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. Cambria E, Grassi M, Hussain A, Havasi C. Sentic computing for social media marketing. Multimed Tools Appl. 2012;59(2):557–77.

    Article  Google Scholar 

  8. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications, vol. 2. Heidelberg: Springer; 2012.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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. 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.

    Article  Google Scholar 

  14. Das D, Bandyopadhyay S. Sentence-level emotion and valence tagging. Cognit Comput. 2012;4(4):420–35.

    Article  Google Scholar 

  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.

  16. Fellbaum C. WordNet: an electronic lexical database. Cambridge, MA: The MIT Press; 1998.

    Google Scholar 

  17. Gangemi A, Presutti V. Towards a pattern science for the semantic web. Semant. Web. 2010;1(1,2):61–8.

    Google Scholar 

  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.

  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. 147–48

  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. 755–60

  21. Johansson R, Moschitti A. Relational features in fine-grained opinion analysis. Comput Linguist. 2013;39(3):473–509.

    Article  Google Scholar 

  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. Kazemzadeh A, Lee S, Narayanan SS. Fuzzy logic models for the meaning of emotion words. IEEE Comput Intell Mag. 2013;8(2):34–49.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  26. Levin B. English verb classes and alternations a preliminary investigation. Chicago: University of Chicago Press; 1993.

    Google Scholar 

  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.

    Article  Google Scholar 

  28. Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. Morgan&Claypool Publishers; 2012.

  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.

  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.

  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.

  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.

  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.

  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. 508–24

  35. Semantic Engines LLC. Opinion crawl., 2010.

  36. Sentiment 140., 2013.

  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.

  38. Social mention., 2011.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  45. University of Stanford. Stanford Sentiment Analysis., 2014.

  46. Wiebe J, Wilson T, Cardie C. Annotating expressions of opinions and emotions in language. Lang Resour Eval. 2005;39(2–3):165–210.

    Article  Google Scholar 

  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. 347–54

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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).

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Correspondence to Diego Reforgiato Recupero.

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Reforgiato Recupero, D., Presutti, V., Consoli, S. et al. Sentilo: Frame-Based Sentiment Analysis. Cogn Comput 7, 211–225 (2015).

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