IEEE Computational Intelligence Magazine - November 2019 - 88

[4] E. Cambria, "Affective computing and sentiment
analysis," IEEE Intell. Syst., vol. 31, no. 2, pp. 102-107,
Mar. 2016.
[5] S. Poria, E. Cambria, D. Hazarika, and P. Vij, "A
deeper look into sarcastic tweets using deep convolutional neural networks," in Proc. 26th Int. Conf. Computational
Linguistics: Technical Papers, Dec. 2016, pp. 1601-1612.
[6] J. M. Pierrel, Ingéniérie des Langues. Hermès, 2000.
[7] P. Schoentjes, Poetique de l'ironie. Paris: Seuil, 2001.
[8] A. Gangemi, M. Alam, and V. Presutti, "Word frame
disambiguation: Evaluating linguistic linked data on
frame detection," in Proc. 4th Int. Workshop on Linked
Data for Information Extraction co-located with ISWC, Oct.
2016, pp. 23-31.
[9] A. Moro, A. Raganato, and R. Navigli, "Entity
linking meets word sense disambiguation: A unified approach," Trans. Assoc. Comput. Linguist., vol. 2, pp. 231-
244, Dec. 2014.
[10] J. Ling and R. Klinger, "An empirical, quantitative
analysis of the differences between sarcasm and irony,"
in Proc. 13th Extended Semantic Web Conf. 2016 Satellite
Events, 2016, pp. 203-216.
[11] D. Reforgiato and E. Cambria, "ESWC'14 challenge
on concept-level sentiment analysis," Commun. Comput.
Inf. Sci., vol. 475, pp. 3-20, Oct. 2014.
[12] M. Dragoni and D. Reforgiato, "Challenge on finegrained sentiment analysis within ESWC 2016," Commun. Comput. Inf. Sci., vol. 641, pp. 79-94, Oct. 2016.
[13] M. Dragoni, A. G. B. Tettamanzi, and C. da Costa
Pereira, "Propagating and aggregating fuzzy polarities for
concept-level sentiment analysis," Cogn. Comput., vol. 7,
no. 2, pp. 186-197, Oct. 2015.
[14] H. Saif, Y. He, and H. Alani, "Semantic sentiment
analysis of Twitter," in Proc. Int. Semantic Web Conf., 2012.
[15] D. R. Recupero, S. Consoli, A. Gangemi, A. G.
Nuzzolese, and D. Spampinato, "A semantic web based
core engine to efficiently perform sentiment analysis," in
The Semantic Web: ESWC 2014 Satellite Events - ESWC
2014 Satellite Events, 2014, pp. 245-248.
[16] A. Dridi and D. R. Recupero, "Leveraging semantics for sentiment polarity detection in social media," Int.
J. Mach. Learn. Cybern., vol. 10, no. 8, pp. 2199-2207,
Sept. 2019.
[17] E. Cambria and A. Hussain, Sentic Computing: A
Common-Sense-Based Framework for Concept-Level Sentiment Analysis, 1st ed. New York: Springer-Verlag, 2015.
[18] S. Attardo, "Irony markers and functions: Towards a
goal-oriented theory of irony and its processing," Rask,
vol. 12, no. 1, pp. 3-20, Jan. 2000.
[19] F. Barbieri and H. Saggion, "Automatic detection of
irony and humour in Twitter," in Proc. 5th Int. Conf. Computational Creativity, June 2014, pp. 155-162.

Guest Editorial

Proc. 52nd Annu. Meeting Association Computational Linguistics, June 2014, vol. 2, pp. 427-433.
[35] M. Cuadros, L. Padró, and G. Rigau, "Highlighting relevant concepts from topic signatures," in Proc. 8th
Int. Conf. Language Resources and Evaluation, LREC, May
2012, pp. 3841-3848.
[36] A. Gangemi and P. Mika, "Understanding the semantic web through descriptions and situations," in Proc.
Move to Meaning ful Internet Systems 2003: CoopIS, DOA,
and ODBASE - OTM Confederated Int. Conf., Nov. 2003,
pp. 689-706.
[37] M. L. de Lacalle, E. Laparra, and G. Rigau, "Predicate matrix: Extending SemLink through WordNet mappings," in Proc. 9th Int. Conf. Language Resources and Evaluation (LREC), May 2014, pp. 903-909.
[38] J. Rouces, G. de Melo, and K. Hose, "FrameBase:
Representing n-ary relations using semantic frames," in
Proc. 12th Extended Semantic Web Conf., 2015, pp. 505-521.
[39] E. Agirre and A. Soroa, "Personalizing pagerank for
word sense disambiguation," in Proc. 12th Conf. European
Chapter Association Computational Linguistics. The Association
Computer Linguistics, Mar. 30-Apr. 3, 2009, pp. 33-41.
[40] A. Ghosh et al., "Semeval-2015 task 11: Sentiment
analysis of figurative language in Twitter," in Proc. 9th
Int. Workshop Semantic Evaluation (SemEval 2015), 2015,
pp. 470-478.
[41] F. Kunneman, C. Liebrecht, M. van Mulken, and A.
van den Bosch, "Signaling sarcasm: From hyperbole to
hashtag," Inform. Process. Manage., vol. 51, no. 4, pp. 500-
509, July 2015.
[42] P. Ristoski and H. Paulheim, "RDF2vec: RDF
graph embeddings for data mining," in Proc. Int. Semantic
Web Conf., 2016.
[43] M. Alam, D. R. Recupero, M. Mongiovì, A. Gangemi, and P. Ristoski, "Event-based knowledge reconciliation using frame embeddings and frame similarity,"
Knowledge-Based Syst., vol. 135, pp. 192-203, Nov. 2017.
[44] E. Filatova, "Irony and sarcasm: Corpus generation
and analysis using crowdsourcing," in Proc. 8th Int. Conf.
Language Resources and Evaluation, LREC, May 2012,
pp. 392-398.
[45] E. Cambria, S. Poria, D. Hazarika, and K. Kwok,
"SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings," in Proc.
32nd AAAI Conf. Artificial Intelligence, Feb. 2018, pp. 1795-
1802.
[46] M. Dragoni, S. Poria, and E. Cambria, "OntoSenticNet: A commonsense ontology for sentiment analysis,"
IEEE Intell. Syst., vol. 33, no. 3, pp. 77-85, June 2018.

(continued from page 11)

is prototyped on low-cost and ubiquitous
WiFi devices and evaluated in extensive
real-world experiments. Experimental
results verify the effectiveness of BeSense
in recognizing user behaviors.
The last paper, "ADMM Empowered Distributed Computational Intelligence for Internet of Energy" by W.
Zhong et al., proposes an approach that
employs Alternating Direction Method
of Multipliers (ADMM) as the theoreti88

[20] A. Reyes, P. Rosso, and D. Buscaldi, "From humor recognition to irony detection: The figurative language of social media," Data Knowl. Eng., vol. 74, pp. 1-12, Apr. 2012.
[21] A. Reyes, P. Rosso, and T. Veale, "A multidimensional approach for detecting irony in Twitter," Lang. Resour. Eval., vol. 47, no. 1, pp. 239-268, Mar. 2013.
[22] D. Hazarika, S. Poria, S. Gorantla, E. Cambria, R.
Zimmermann, and R. Mihalcea, "CASCADE: Contextual
sarcasm detection in online discussion forums," in Proc. 27th
Int. Conf. Computational Linguistics, 2018, pp. 1837-1848.
[23] N. Majumder, S. Poria, H. Peng, N. Chhaya, E.
Cambria, and A. F. Gelbukh, "Sentiment and sarcasm
classification with multitask learning," IEEE Intell. Syst.,
vol. 34, no. 3, pp. 38-43, Jan. 2019.
[24] A. V. Toropova, "Irony detection based on semantic
similarity," Tr. SPIIRAN, vol. 1, pp. 178-193, Aug. 2014.
[25] A. Gangemi, M. Alam, L. Asprino, V. Presutti, and
D. R. Recupero, "Framester: A wide coverage linguistic
linked data hub," in Proc. 20th Int. Conf. Knowledge Engineering and Knowledge Management, Nov. 2016.
[26] C. F. Baker, C. J. Fillmore, and J. B. Lowe, "The
Berkeley framenet project," in Proc. 36th Annu. Meeting
Association Computational Linguistics and 17th Int. Conf.
Computational Linguistics, Aug. 1998, vol. 1, pp. 86-90.
[27] C. Fellbaum, "Review of " WordNet: An electronic
lexical database" by Christiane Fellbaum, The MIT Press
1998," Comput. Linguist., vol. 25, no. 2, pp. 292-296,
June 1999.
[28] R. Navigli and S. P. Ponzetto, "BabelNet: The automatic construction, evaluation and application of a widecoverage multilingual semantic network," Artif. Intell.,
vol. 193, pp. 217-250, Dec. 2012.
[29] A. G. Nuzzolese, A. Gangemi, and V. Presutti,
"Gathering lexical linked data and knowledge patterns
from FrameNet," in Proc. 6th Int. Conf. Knowledge Capture
(K-CAP), 2011, pp. 41-48.
[30] A. Gangemi, R. Navigli, and P. Velardi, "The OntoWordNet Project: Extension and axiomatization of
conceptual relations in WordNet," in Proc. Move to Meaning ful Internet Systems 2003: CoopIS, DOA, and ODBASE
- OTM Confederated Int. Conf., Nov. 2003, pp. 820-838.
[31] K. K. Schuler, "VerbNet: A broad-coverage, comprehensive verb lexicon," Ph.D. dissertation, 2005.
[32] A. Gangemi, N. Guarino, C. Masolo, and A. Oltramari, "Sweetening WORDNET with DOLCE," AI
Mag., vol. 24, no. 3, pp. 13-24, Sept. 2003.
[33] S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3.0: An enhanced lexical resource for sentiment
analysis and opinion mining," in Proc. Int. Conf. Language
Resources and Evaluation, LREC, May 2010, pp. 17-23.
[34] J. Staiano and M. Guerini, "Depeche mood: A lexicon for emotion analysis from crowd annotated news," in

cal framework for the design of distributed computational intelligence in
Internet of Energy (IoE). The authors
discuss the challenges of ADMM implementation in IoE and propose a joint
computing and networking resources
management architecture to meet the
challenges. Numerical results show that
this architecture could reduce the computing and communication costs of
ADMM implementation.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2019

The guest editors would like to
thank all the authors for their contributions to the special issue, and all
reviewers for their voluntary services.
Special thanks go to the Editor-inChief, Prof. Hisao Ishibuchi, and all the
members of the editorial team for their
support during the editing process of
this Special Issue.
IEEE Computational Intelligence Magazine - November 2019

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2019
