- -

DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing

Mostrar el registro completo del ítem

Distante, D.; Faralli, S.; Rittinghaus, S.; Rosso, P.; Samsami, N. (2022). DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing. Cognitive Computation. 14(1):62-77. https://doi.org/10.1007/s12559-021-09825-w

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/190207

Ficheros en el ítem

Thumbnail
Nombre: DomainSenticNet_v ...
Tamaño: 3.781Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: DistanteFaralliRi ...
Tamaño: 3.119Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing
Autor: Distante, Damiano Faralli, Stefano Rittinghaus, Steve Rosso, Paolo Samsami, Nima
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. ...[+]
Palabras clave: Sentic computing , SenticNet , OntoSenticNet , Kickstarter , Interpretability , Opinion mining , Marketing
Derechos de uso: Reserva de todos los derechos
Fuente:
Cognitive Computation. (issn: 1866-9956 )
DOI: 10.1007/s12559-021-09825-w
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s12559-021-09825-w
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096212-B-C31/ES/DESINFORMACION Y AGRESIVIDAD EN SOCIAL MEDIA: AGREGANDO INFORMACION Y ANALIZANDO EL LENGUAJE/
Agradecimientos:
The work of Paolo Rosso was partially funded by the Spanish MICINN under the project PGC2018-096212-B-C31.
Tipo: Artículo

References

Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble [application notes]. Computer Intelligence Magazine. 2020;15 1:64–75. https://doi.org/10.1109/MCI.2019.2954667

Alhussien I, Cambria E, NengSheng Z. Semantically enhanced models for commonsense knowledge acquisition. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), p. 1014–1021. November 17-20, Singapore (2018). https://doi.org/10.1109/ICDMW.2018.00146

Angulo C, Falomir IZ, Anguita D, Agell N, Cambria E. Bridging cognitive models and recommender systems. Cogn Comput 12(2), 426–427 (2020). https://doi.org/10.1007/s12559-020-09719-3 [+]
Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble [application notes]. Computer Intelligence Magazine. 2020;15 1:64–75. https://doi.org/10.1109/MCI.2019.2954667

Alhussien I, Cambria E, NengSheng Z. Semantically enhanced models for commonsense knowledge acquisition. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), p. 1014–1021. November 17-20, Singapore (2018). https://doi.org/10.1109/ICDMW.2018.00146

Angulo C, Falomir IZ, Anguita D, Agell N, Cambria E. Bridging cognitive models and recommender systems. Cogn Comput 12(2), 426–427 (2020). https://doi.org/10.1007/s12559-020-09719-3

Bandari S, Bulusu VV. Survey on ontology-based sentiment analysis of customer reviews for products and services. In: K.S. Raju, R. Senkerik, S.P. Lanka, V. Rajagopal (eds.) Data Engineering and Communication Technology, vol. 1079, pp. 91–101. Springer Singapore, Singapore (2020). https://doi.org/10.1007/978-981-15-1097-7_8

Billal B, Fonseca A, Sadat F, Lounis H. Semi-supervised learning and social media text analysis towards multi-labeling categorization. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1907–1916. December 11-14, Boston, MA, USA (2017). https://doi.org/10.1109/BigData.2017.8258136

Cambria E, Grassi M, Hussain A, Havasi C. Sentic computing for social media marketing. Multimed Tools Appl 59(2), 557–577 (2012). https://doi.org/10.1007/s11042-011-0815-0

Cambria E, Hussain A, Havasi C, Eckl C. Sentic Computing: Exploitation of Common Sense for the Development of Emotion-Sensitive Systems, Lecture Notes in Computer Science, vol. 5967, pp. 148–156. Springer Berlin Heidelberg, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12397-9_12

Cambria E, Li Y, Xing FZ, Poria S, Kwok K. Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, p. 105–114. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3340531.3412003

Cambria E, Livingstone A, Hussain A. The hourglass of emotions. In: A. Esposito, A.M. Esposito, A. Vinciarelli, R. Hoffmann, V.C. Müller (eds.) Cognitive Behavioural Systems, COST 2012 International Training School, vol. 7403, pp. 144–157. Springer Berlin Heidelberg (2012). https://doi.org/10.1007/978-3-642-34584-5_11

Cambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst 32(06), 74–80 (2017). https://doi.ieeecomputersociety.org/10.1109/MIS.2017.4531228

Cambria E, Poria S, Hazarika D, Kwok K. Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In: S.A. McIlraith, K.Q. Weinberger (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), pp. 1795–1802. AAAI Press, New Orleans, Louisiana, USA (2018). https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16839

Chakraborty K, Bhattacharyya S, Bag R. A survey of sentiment analysis from social media data. IEEE Transactions on Computational Social Systems 7(2), 450–464 (2020). https://doi.org/10.1109/TCSS.2019.2956957

Chauhan GS, Meena YK. Domsent: Domain-specific aspect term extraction in aspect-based sentiment analysis. In: A.K. Somani, R.S. Shekhawat, A. Mundra, S. Srivastava, V.K. Verma (eds.) Smart Systems and IoT: Innovations in Computing, vol. 141, pp. 103–109. Springer Singapore, Singapore (2020). https://doi.org/10.1007/978-981-13-8406-6_11

Dragoni M, Poria S, Cambria E. Ontosenticnet: A commonsense ontology for sentiment analysis. IEEE Intell Syst 33, 77–85 (2018). https://doi.org/10.1109/MIS.2018.033001419

van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learn 109(2), 373–440 (2020). https://doi.org/10.1007/s10994-019-05855-6

Faralli S, Rittinghaus S, Samsami N, Distante D, Rocha E. Emotional intensity-based success prediction model for crowdfunded campaigns. Inf Process Manag 58(1), article ID 102394 (2021). https://doi.org/10.1016/j.ipm.2020.102394

Faralli S, Velardi P, Yusifli F. Multiple knowledge GraphDB (MKGDB). In: Proceedings of The 12th Language Resources and Evaluation Conference, pp. 2325–2331. European Language Resources Association, Marseille, France (2020). https://www.aclweb.org/anthology/2020.lrec-1.283

Fellbaum C. (ed.): WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge, MA (1998)

Fernandez-Breis JT, Qazi A, Raj RG, Tahir M, Cambria E, Syed KBS. Enhancing business intelligence by means of suggestive reviews. Sci World J vol. 2014, article ID 879323 (2014). https://doi.org/10.1155/2014/879323

Hussain A, Cambria E. Semi-supervised learning for big social data analysis. Neurocomputing 275, 1662 – 1673 (2018). https://doi.org/10.1016/j.neucom.2017.10.010

Khatua A, Cambria E. A tale of two epidemics: Contextual word2vec for classifying twitter streams during outbreaks. Inf Process Manag 56(1), 247 – 257 (2019). https://doi.org/10.1016/j.ipm.2018.10.010

Kumar A, Srinivasan K, Cheng WH, Zomaya AY. Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf Process Manag 57(1), article ID 102141 (2020). https://doi.org/10.1016/j.ipm.2019.102141

Li H, Armiento R, Lambrix P. A method for extending ontologies with application to the materials science domain. Data Science Journal 18, 1–21 (2019). https://doi.org/10.5334/dsj-2019-050

Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. In: AAAI Conference on Artificial Intelligence, pp. 5876–5883 (2018). https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16541

Me D, Frasincar F. Aldonar: A hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalized domain ontology and a regularized neural attention model. Inf Process Manag 57(3), article ID 102211 (2020). https://doi.org/10.1016/j.ipm.2020.102211

Nguyen HT, Duong PH, Cambria E. Learning short-text semantic similarity with word embeddings and external knowledge sources. Knowledge-Based Systems 182, article ID 104842 (2019). http://www.sciencedirect.com/science/article/pii/S095070511930317X

Paulheim H. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017). https://doi.org/10.3233/SW-160218

Pearl J, Mackenzie D. The Book of Why. Basic Books, New York (2018). https://dl.acm.org/doi/book/10.5555/3238230

Plutchik R. The nature of emotions. Am Sci 89(4), 344–350 (2001). https://www.jstor.org/stable/27857503

Rajagopal D, Cambria E, Olsher D, Kwok K. A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd International Conference on World Wide Web, WWW ’13 Companion, p. 565–570. Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2487788.2487995

Saif H, Fernandez M, He Y, Alani H. Senticircles for contextual and conceptual semantic sentiment analysis of twitter. In: V. Presutti, C. d’Amato, F. Gandon, M. d’Aquin, S. Staab, A. Tordai (eds.) The Semantic Web: Trends and Challenges, pp. 83–98. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_7

Sharma A, Kiciman E. DoWhy: A Python package for causal inference (2019). https://github.com/microsoft/dowhy

Shiller R. Narrative economics. Am Econ Rev 107, 967–1004 (2017). https://doi.org/10.1257/aer.107.4.967

Song Y, Wang H, Wang Z, Li H, Chen W. Short text conceptualization using a probabilistic knowledgebase. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Three, IJCAI’11, p. 2330-2336. AAAI Press, Barcelona, Catalonia, Spain (2011)

Susanto Y, Livingstone AG, Ng BC, Cambria E. The hourglass model revisited. IEEE Intell Syst 35(5), 96–102 (2020). https://doi.org/10.1109/MIS.2020.2992799

Wu W, Li H, Wang H, Zhu KQ. Probase: A probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD, p. 481–492. Association for Computing Machinery, New York, NY, USA (2012). https://doi.org/10.1145/2213836.2213891

Xia Y, Cambria E, Hussain A, Zhao H. Word polarity disambiguation using bayesian model and opinion-level features. Cogn Comput 7(3), 369–380 (2015). https://doi.org/10.1007/s12559-014-9298-4

Xing FZ, Cambria E, Welsch RE. Natural language based financial forecasting: a survey. Artif Intell Rev 50(1), 49–73 (2018). https://doi.org/10.1007/s10462-017-9588-9

Zhuang L, Schouten K, Frasincar F. Soba: Semi-automated ontology builder for aspect-based sentiment analysis. Journal of Web Semantics 60, article ID 100544 (2019). https://doi.org/10.1016/j.websem.2019.100544

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem