Sentilo: Frame-Based Sentiment Analysis

Cognitive Computation

Volumn 7, Issue 2, 2015, Pages 211-225

  Reforgiato Recupero, Diego ^a   Presutti, Valentina ^a   Consoli, Sergio ^a   Gangemi, Aldo ^a,b   Nuzzolese, Andrea Giovanni ^a,c  

^a INSTITUTE OF COGNITIVE SCIENCES AND TECHNOLOGIES   (Italy)

^b UNIVERSITÉ PARIS 13   (France)

^c UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Conceptual frames; Opinion mining; Sentic computing; Sentiment analysis

Indexed keywords

DATA MINING; SEMANTIC WEB;

CONCEPTUAL FRAMES; DOMAIN INDEPENDENTS; LEXICAL RESOURCES; NATURAL LANGUAGE PROCESSING; OPINION MINING; SEMANTIC WEB TECHNOLOGY; SENTIC COMPUTING; SENTIMENT ANALYSIS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84943014678     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-014-9302-z     Document Type: Article

References (47)

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.
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.
7. Cambria E, Grassi M, Hussain A, Havasi C. Sentic computing for social media marketing. Multimed Tools Appl. 2012;59(2):557–77.
8. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications, vol. 2. Heidelberg: Springer; 2012.
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.
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.
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.
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.
14. Das D, Bandyopadhyay S. Sentence-level emotion and valence tagging. Cognit Comput. 2012;4(4):420–35.
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.
17. Gangemi A, Presutti V. Towards a pattern science for the semantic web. Semant. Web. 2010;1(1,2):61–8.
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.
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.
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.
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.
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.
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.
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. http://opinioncrawl.com/, 2010.
36. Sentiment 140. http://www.sentiment140.com/, 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. http://www.socialmention.com/, 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.
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. http://nlp.stanford.edu/sentiment/, 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.
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