. As-an-example, > <rdf:type rdf:resource=>a lot of fun</text> <semantics xmlns="http://sentic.net/api" rdf:resource="http://sentic.net/api/en/concept/radical"/> <semantics xmlns="http://sentic.net/api" rdf:resource="http://sentic.net/api/en/concepthttp://sentic.net/api" rdf:resource="http://senticsentic.net/api" rdf:resource="http://sentichttp://sentic.net/api" rdf:resource="http://sentic, Sentic- Net is shown below: <rdf:Description rdf>0.856</aptitude> References 1. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.79-86, 2001.

B. Liu and L. Zhang, A Survey of Opinion Mining and Sentiment Analysis, pp.415-463, 2012.
DOI : 10.1007/978-1-4614-3223-4_13

J. Blitzer, M. Dredze, and F. Pereira, Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, In: ACL, pp.187-205, 2007.

B. Pang and L. Lee, Opinion Mining and Sentiment Analysis, Foundations and Trends?? in Information Retrieval, vol.2, issue.1???2, pp.1-135, 2008.
DOI : 10.1561/1500000011

B. Pang and L. Lee, A sentimental education, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics , ACL '04, pp.271-278, 2004.
DOI : 10.3115/1218955.1218990

K. Dave, S. Lawrence, and D. M. Pennock, Mining the peanut gallery, Proceedings of the twelfth international conference on World Wide Web , WWW '03, pp.519-528, 2003.
DOI : 10.1145/775152.775226

G. Paltoglou and M. Thelwall, A study of information retrieval weighting schemes for sentiment analysis, In: ACL, pp.1386-1395, 2010.

S. Tan, Y. Wang, and X. Cheng, Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.743-744, 2008.
DOI : 10.1145/1390334.1390481

L. Qiu, W. Zhang, C. Hu, and K. Zhao, SELC, Proceeding of the 18th ACM conference on Information and knowledge management, CIKM '09, pp.929-936, 2009.
DOI : 10.1145/1645953.1646072

P. Melville, W. Gryc, and R. D. Lawrence, Sentiment analysis of blogs by combining lexical knowledge with text classification, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.1275-1284, 2009.
DOI : 10.1145/1557019.1557156

M. Taboada, J. Brooke, M. Tofiloski, K. D. Voll, and M. Stede, Lexicon-Based Methods for Sentiment Analysis, Computational Linguistics, vol.25, issue.3, pp.267-307, 2011.
DOI : 10.1007/s10579-005-7880-9

P. D. Turney, Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews, In: ACL, pp.417-424, 2002.

S. Somasundaran, Discourse-level relations for Opinion Analysis, 2010.

N. Asher, F. Benamara, and Y. Y. Mathieu, Distilling opinion in discourse: A preliminary study, In: COLING (Posters), pp.7-10, 2008.
URL : https://hal.archives-ouvertes.fr/halshs-00751060

H. Wang and G. Zhou, Topic-Driven Multi-document Summarization, 2010 International Conference on Asian Language Processing, pp.195-198, 2010.
DOI : 10.1109/IALP.2010.26

E. Riloff, S. Patwardhan, and J. Wiebe, Feature subsumption for opinion analysis, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pp.440-448, 2006.
DOI : 10.3115/1610075.1610137

J. Wiebe, T. Wilson, R. F. Bruce, M. Bell, and M. Martin, Learning Subjective Language, Computational Linguistics, vol.25, issue.4, pp.277-308, 2004.
DOI : 10.1109/PROC.1986.13641

T. Wilson, J. Wiebe, and R. Hwa, Just how mad are you? finding strong and weak opinion clauses, In: AAAI, pp.761-769, 2004.

T. Wilson, J. Wiebe, and R. Hwa, RECOGNIZING STRONG AND WEAK OPINION CLAUSES, Computational Intelligence, vol.20, issue.2, pp.73-99, 2006.
DOI : 10.1162/0891201041850885

H. Yu and V. Hatzivassiloglou, Towards answering opinion questions, Proceedings of the 2003 conference on Empirical methods in natural language processing -, pp.129-136, 2003.
DOI : 10.3115/1119355.1119372

V. Hatzivassiloglou and J. Wiebe, Effects of adjective orientation and gradability on sentence subjectivity, Proceedings of the 18th conference on Computational linguistics -, pp.299-305, 2000.
DOI : 10.3115/990820.990864

S. M. Kim and E. H. Hovy, Crystal: Analyzing predictive opinions on the web, In: EMNLP- CoNLL, pp.1056-1064, 2007.

S. M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti, Automatically assessing review helpfulness, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pp.423-430, 2006.
DOI : 10.3115/1610075.1610135

N. Jakob and I. Gurevych, Extracting opinion targets in a single and cross-domain setting with conditional random fields, In: EMNLP, pp.1035-1045, 2010.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, In: ICML, pp.282-289, 2001.

D. Freitag and A. Mccallum, Information extraction with hmm structures learned by stochastic optimization, In: AAAI/IAAI, pp.584-589, 2000.

W. Jin and H. H. Ho, A novel lexicalized HMM-based learning framework for web opinion mining, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.465-472, 2009.
DOI : 10.1145/1553374.1553435

W. Jin, H. H. Ho, and R. K. Srihari, OpinionMiner, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.1195-1204, 2009.
DOI : 10.1145/1557019.1557148

B. Liu, M. Hu, and J. Cheng, Opinion observer, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.342-351, 2005.
DOI : 10.1145/1060745.1060797

Y. Wu, Q. Zhang, X. Huang, and L. Wu, Phrase dependency parsing for opinion mining, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 3, EMNLP '09, pp.1533-1541, 2009.
DOI : 10.3115/1699648.1699700

Q. Su, X. Xu, H. Guo, Z. Guo, X. Wu et al., Hidden sentiment association in chinese web opinion mining, Proceeding of the 17th international conference on World Wide Web , WWW '08, pp.959-968, 2008.
DOI : 10.1145/1367497.1367627

G. Qiu, B. Liu, J. Bu, and C. Chen, Expanding domain sentiment lexicon through double propagation, In: IJCAI, pp.1199-1204, 2009.

G. Qiu, B. Liu, J. Bu, and C. Chen, Opinion Word Expansion and Target Extraction through Double Propagation, Computational Linguistics, vol.37, issue.1, pp.9-27, 2011.
DOI : 10.1162/0891201041850885

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

A. Bermingham and A. F. Smeaton, Classifying sentiment in microblogs, Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, pp.1833-1836, 2010.
DOI : 10.1145/1871437.1871741

A. Go, R. Bhayani, and L. Huang, Twitter sentiment classification using distant supervision, CS224N Project Report, 2009.

E. Cambria and A. Hussain, of SpringerBriefs in Cognitive Computation, Sentic Computing: Techniques, Tools, and Applications, 2012.

E. Cambria and A. Hussain, Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System, Cognitive Computation, vol.39, issue.2, pp.477-496, 2012.
DOI : 10.1007/s12559-012-9145-4

Q. F. Wang, E. Cambria, C. L. Liu, and A. Hussain, Common Sense Knowledge for Handwritten Chinese Text Recognition, Cognitive Computation, vol.9, issue.1, pp.234-242, 2013.
DOI : 10.1007/s12559-012-9183-y

A. Aue and M. Gamon, Customizing sentiment classifiers to new domains: a case study, Proceedings of RANLP, 2005.

H. Yang, J. Callan, and L. Si, Knowledge transfer and opinion detection in the TREC 2006 blog track, In: TREC, 2006.

S. J. Pan, X. Ni, J. T. Sun, Q. Yang, and Z. Chen, Cross-domain sentiment classification via spectral feature alignment, Proceedings of the 19th international conference on World wide web, WWW '10, pp.751-760, 2010.
DOI : 10.1145/1772690.1772767
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.181.20

D. Bollegala, D. J. Weir, and J. A. Carroll, Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.8, pp.1719-1731, 2013.
DOI : 10.1109/TKDE.2012.103

R. Xia, C. Zong, X. Hu, and E. Cambria, Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification, IEEE Intelligent Systems, vol.28, issue.3, pp.10-18, 2013.
DOI : 10.1109/MIS.2013.27

Y. Yoshida, T. Hirao, T. Iwata, M. Nagata, and Y. Matsumoto, Transfer learning for multipledomain sentiment analysis?identifying domain dependent/independent word polarity, In: AAAI, pp.1286-1291, 2011.

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

A. C. Tsai, C. E. Wu, R. T. Tsai, and J. Y. Jen-hsu, Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge, IEEE Intelligent Systems, vol.28, issue.2, pp.22-30, 2013.
DOI : 10.1109/MIS.2013.25

Y. J. Tai and H. Y. Kao, Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation, Proceedings of International Conference on Information Integration and Web-based Applications & Services, IIWAS '13, pp.53-5362, 2013.
DOI : 10.1145/2539150.2539190

S. Huang, Z. Niu, and C. Shi, Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl.-Based Syst, pp.56-191, 2014.

L. A. Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.
DOI : 10.1016/S0019-9958(65)90241-X

L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, vol.1, issue.1, pp.3-28, 1978.
DOI : 10.1016/0165-0114(78)90029-5

L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning???I, Information Sciences, vol.8, issue.3, pp.199-249, 1975.
DOI : 10.1016/0020-0255(75)90036-5

H. Hellendoorn and C. Thomas, Defuzzification in fuzzy controllers, Intelligent and Fuzzy Systems, vol.1, pp.109-123, 1993.

C. Fellbaum, WordNet: An Electronic Lexical Database, 1998.

E. Cambria, D. Olsher, and D. Rajagopal, Senticnet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis, In: AAAI, pp.1515-1521, 2014.

S. Baccianella, A. Esuli, and F. Sebastiani, Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, In: LREC, pp.2200-2204, 2010.

M. Grassi, Developing HEO Human Emotions Ontology, COST 2101/2102 Conference, pp.244-251, 2009.
DOI : 10.1007/978-3-642-04391-8_32

W. Dong, H. Shah, and F. Wong, Fuzzy computations in risk and decision analysis, Civil Engineering Systems, vol.13, issue.7, pp.201-208, 1985.
DOI : 10.1080/02630258508970407

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-2727, 2011.
DOI : 10.1145/1961189.1961199

A. K. Mccallum, Mallet: A machine learning for language toolkit, 2002.