- -

A Knowledge-Based Model for Polarity Shifters

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A Knowledge-Based Model for Polarity Shifters

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Blazquez-Lopez - A ...
Tamaño: 1.582Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Blázquez-López, Yolanda es_ES
dc.date.accessioned 2023-01-09T08:34:25Z
dc.date.available 2023-01-09T08:34:25Z
dc.date.issued 2022-11-23
dc.identifier.uri http://hdl.handle.net/10251/191103
dc.description.abstract [EN] Polarity shifting can be considered one of the most challenging problems in the context of Sentiment Analysis. Polarity shifters, also known as contextual valence shifters (Polanyi and Zaenen 2004), are treated as linguistic contextual items that can increase, reduce or neutralise the prior polarity of a word called focus included in an opinion. The automatic detection of such items enhances the performance and accuracy of computational systems for opinion mining, but this challenge remains open, mainly for languages other than English. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets, both in English and Spanish. To this end, we describe a novel knowledge-based model to deal with three dimensions of contextual shifters: negation, quantification, and modality (or irrealis). es_ES
dc.description.sponsorship This work is part of the project grant PID2020-112827GB-I00, funded by MCIN/AEI/10.13039/501100011033, and the SMARTLAGOON project [101017861], funded by Horizon 2020 - European Union Framework Programme for Research and Innovation. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Journal of Computer-Assisted Linguistic Research es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Opinion mining es_ES
dc.subject Sentiment analysis es_ES
dc.subject Polarity shifting es_ES
dc.subject Negation es_ES
dc.subject Quantification es_ES
dc.subject Modality es_ES
dc.title A Knowledge-Based Model for Polarity Shifters es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/jclr.2022.18807
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112827GB-I00/ES/SISTEMA INTELIGENTE MULTIMODAL BASADO EN CROWDSENSING PARA UN SERVICIO DE PREDICCION DE PROBLEMAS SOCIALES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101017861 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Blázquez-López, Y. (2022). A Knowledge-Based Model for Polarity Shifters. Journal of Computer-Assisted Linguistic Research. 6:87-107. https://doi.org/10.4995/jclr.2022.18807 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/jclr.2022.18807 es_ES
dc.description.upvformatpinicio 87 es_ES
dc.description.upvformatpfin 107 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.identifier.eissn 2530-9455
dc.relation.pasarela OJS\18807 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Commission es_ES
dc.description.references Andreevskaia, Alina, and Sabine Bergler. 2007. "CLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment tagging." Paper presented at Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), USA: 117-120. https://doi.org/10.3115/1621474.1621496 es_ES
dc.description.references Asmi, Amna, and Tanko Ishaya. 2012. "Negation identification and calculation in sentiment analysis." Paper presented at The second international conference on advances in information mining and management: 1-7. es_ES
dc.description.references Ayeste, Zahra, and Samira Noferesti. 2022. "A semantic approach based on domain knowledge for polarity shift detection using distant supervision." Progress in Artificial Intelligence 11 : 169-80. https://doi.org/10.1007/s13748-021-00267-x es_ES
dc.description.references Barnes, Jeremy, Erik Velldal, and Lilja Øvrelid. 2021. "Improving sentiment analysis with multi-task learning of negation." Natural Language Engineering 27 (2): 249-69. https://doi.org/10.1017/S1351324920000510 es_ES
dc.description.references Benamara, Farah, Maite Taboada, and Yannick Mathieu. 2017. "Evaluative language beyond bags of words: Linguistic insights and computational applications." Computational Linguistics 43 (1): 201-65. doi: 10.1162/COLI_a_00278. https://doi.org/10.1162/COLI_a_00278 es_ES
dc.description.references Bos, Thomas, and Flavius Frasincar. 2021. "Automatically building financial sentiment lexicons while accounting for negation." Cognitive Computation 14 (1): 442-60. https://doi.org/10.1007/s12559-021-09833-w es_ES
dc.description.references Carrillo-de-Albornoz, Jorge, and Laura Plaza. 2013. "An emotion-based model of negation, intensifiers, and modality for polarity and intensity classification." Journal of the American Society for Information Science and Technology 64 (8): 1618-33. https://doi.org/10.1002/asi.22859 es_ES
dc.description.references Cruz, Noa P., Maite Taboada, and Ruslan Mitkov. 2016. "A machine-learning approach to negation and speculation detection for sentiment analysis." Journal of the Association for Information Science and Technology 67 (9): 2118-36. https://doi.org/10.1002/asi.23533 es_ES
dc.description.references Dave, Kushal, Steve Lawrence, and David M. Pennock. 2003. "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews." Paper presented at Proceedings of the 12th international conference on World Wide Web, New York : 519-528. https://doi.org/10.1145/775152.775226 es_ES
dc.description.references Diamantini, Claudia, Alex Mircoli, and Domenico Potena. 2016. "A negation handling technique for sentiment analysis." Paper presented at 2016 International conference on collaboration technologies and systems (CTS): 188-195. https://doi.org/10.1109/CTS.2016.0048 es_ES
dc.description.references Edmonds, Philip, and Graeme Hirst. 2002. "Near-synonymy and lexical choice." Computational Linguistics - Association for Computational Linguistics 28 (2): 105-44. https://doi.org/10.1162/089120102760173625 es_ES
dc.description.references Esuli, Andrea, and Fabrizio Sebastiani. 2006. "Sentiwordnet: A publicly available lexical resource for opinion mining." Paper presented at Proceedings of the fifth international conference on language resources and evaluation (LREC'06), Genoa, Italy : 417-22. es_ES
dc.description.references Hogenboom, A., P. van Iterson, B. Heerschop, F. Frasincar, and U. Kaymak. 2011. "Determining negation scope and strength in sentiment analysis." : 2589-2594. https://doi.org/10.1109/ICSMC.2011.6084066 es_ES
dc.description.references Jiménez-Zafra, Salud María, Roser Morante, Eduardo Blanco, María Teresa Martín-Valdivia, L. Alfonso Ureña-López, Nicoletta Calzolari, Frederic Bechet, et al. 2020. "Detecting negation cues and scopes in spanish." : 6902-6911. es_ES
dc.description.references Kennedy, Alistair, and Diana Inkpen. 2006. "Sentiment classification of movie reviews using contextual valence shifters." Computational Intelligence 22 (2): 110-25. https://doi.org/10.1111/j.1467-8640.2006.00277.x es_ES
dc.description.references Khan, Khairullah, Baharum Baharudin, Aurnagzeb Khan, and Ashraf Ullah. 2014. "Mining opinion components from unstructured reviews: A review." Journal of King Saud University-Computer and Information Sciences 26 (3): 258-75. https://doi.org/10.1016/j.jksuci.2014.03.009 es_ES
dc.description.references Kiritchenko, Svetlana, and Saif M. Mohammad. 2017. "The effect of negators, modals, and degree adverbs on sentiment composition.", San Diego, California. https://doi.org/10.18653/v1/W16-0410 es_ES
dc.description.references Kong, Lingpeng, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer, and Noah A. Smith. 2014. "A dependency parser for tweets." Paper presented at Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP): 1001-1012. https://doi.org/10.3115/v1/D14-1108 es_ES
dc.description.references Lapponi, Emanuele, Jonathon Read, and Lilja Øvrelid. 2012. "Representing and resolving negation for sentiment analysis." Paper presented at 2012 ieee 12th international conference on data mining workshops: 687-692. https://doi.org/10.1109/ICDMW.2012.23 es_ES
dc.description.references Li, Shoushan, and Chu-Ren Huang. 2009. "Sentiment classification considering negation and contrast transition." Paper presented at Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1: 307-316. es_ES
dc.description.references Li, Shoushan, Sophia Yat Mei Lee, Ying Chen, Chu-Ren Huang, and Guodong Zhou. 2010. "Sentiment classification and polarity shifting." Paper presented at Proceedings of the 23rd International Conference on Computational Linguistics: 635-643. es_ES
dc.description.references Li, Shoushan, Zhongqing Wang, Sophia Yat Mei Lee, and Chu-Ren Huang. 2013. "Sentiment classification with polarity shifting detection." Paper presented at 2013 International Conference on Asian Language Processing: 129-132. doi: 10.1109/IALP.2013.44. https://doi.org/10.1109/IALP.2013.44 es_ES
dc.description.references Liu, Bing. 2015. Sentiment analysis: Mining opinions, sentiments, and emotions. New York, USA: Cambridge University Press. https://doi.org/10.1017/CBO9781139084789 es_ES
dc.description.references Lu, Bin, and Benjamin K. Tsou. 2010. "Combining a large sentiment lexicon and machine learning for subjectivity classification." Paper presented at 2010 International Conference on Machine Learning and Cybernetics: 3311-3316. https://doi.org/10.1109/ICMLC.2010.5580672 es_ES
dc.description.references Mäntylä, Mika V., Daniel Graziotin, and Miikka Kuutila. 2018. "The evolution of sentiment analysis.A review of research topics, venues, and top cited papers." Computer Science Review 27: 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002 es_ES
dc.description.references Mathew, Daya Mary, and Hema Krishnan. 2017. "Polarity shift handling techniques: A survey." International Journal of Computer Applications 178 (6). es_ES
dc.description.references Mendon, Shalak, Pankaj Dutta, Abhishek Behl, and Stefan Lessmann. 2021. "A hybrid approach of machine learning and lexicons to sentiment analysis: Enhanced insights from twitter data of natural disasters." Information Systems Frontiers 23 (5): 1145-68. https://doi.org/10.1007/s10796-021-10107-x es_ES
dc.description.references Morante, Roser, and Eduardo Blanco. 2021. "Recent advances in processing negation." Natural Language Engineering 27 (2): 121-30. https://doi.org/10.1017/S1351324920000534 es_ES
dc.description.references Morante, Roser, and Walter Daelemans. 2009. "A metalearning approach to processing the scope of negation." Paper presented at Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009): 21-29. https://doi.org/10.3115/1596374.1596381 es_ES
dc.description.references Moreno-Ortiz, Antonio, and Javier Fernández-Cruz. 2015. "Identifying polarity in financial texts for sentiment analysis: A corpus-based approach." Procedia - Social and Behavioral Sciences 198 : 330-8. https://doi.org/10.1016/j.sbspro.2015.07.451 es_ES
dc.description.references Morsy, Sara A., and Ahmed Rafea. 2012. "Improving document-level sentiment classification using contextual valence shifters." In Natural language processing and information systems., eds. Gosse Bouma, Ashwin Ittoo, Elisabeth Métais and Hans Wortmann. Vol. 7337, 253-258. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-31178-9_30 es_ES
dc.description.references Mukherjee, Partha, Youakim Badr, Shreyesh Doppalapudi, Satish M. Srinivasan, Raghvinder S. Sangwan, and Rahul Sharma. 2021. "Effect of negation in sentences on sentiment analysis and polarity detection." Procedia Computer Science 185 : 370-9. https://doi.org/10.1016/j.procs.2021.05.038 es_ES
dc.description.references Musat, Claudiu, and Stefan Trausan-Matu. 2010. "The impact of valence shifters on mining implicit economic opinions." In . Vol. 6304, 131-140. Berlin, Heidelberg: Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_14 es_ES
dc.description.references Pabón, Oswaldo Solarte, Orlando Montenegro, Maria Torrente, Alejandro Rodríguez-González, Mariano Provencio, and Ernestina Menasalvas. 2022. "Negation and uncertainty detection in clinical texts written in spanish: A deep learning-based approach." PeerJ.Computer Science 8 : e913. https://doi.org/10.7717/peerj-cs.913 es_ES
dc.description.references Pandey, Swastika, Santwana Sagnika, and Bhabani Shankar Prasad Mishra. 2018. "A technique to handle negation in sentiment analysis on movie reviews." Paper presented at 2018 International Conference on Communication and Signal Processing (ICCSP): 737-743. https://doi.org/10.1109/ICCSP.2018.8524421 es_ES
dc.description.references Periñán-Pascual, Carlos, and Francisco Arcas-Túnez. 2017. "A knowledge-based approach to social sensors for environmentally-related problems." Paper presented at Intelligent Environments (Workshops): 49-58. doi: 10.3233/978-1-61499-796-2-49. es_ES
dc.description.references Piryani, Rajesh, D. Madhavi, and Vivek Kumar Singh. 2017. "Analytical mapping of opinion mining and sentiment analysis research during 2000-2015." Information Processing & Management 53 (1): 122-50. https://doi.org/10.1016/j.ipm.2016.07.001 es_ES
dc.description.references Polanyi, Livia, and Annie Zaenen. 2004. "Contextual valence shifters." , Menlo park, California: 106-111, https://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-020.pdf. es_ES
dc.description.references Prabowo, Rudy, and Mike Thelwall. 2009. "Sentiment analysis: A combined approach." Journal of Informetrics 3 (2): 143-57. https://doi.org/10.1016/j.joi.2009.01.003 es_ES
dc.description.references Rahimi, Zeinab, Samira Noferesti, and Mehrnoush Shamsfard. 2018. "Applying data mining and machine learning techniques for sentiment shifter identification." Language Resources and Evaluation 53 (2): 279-302. https://doi.org/10.1007/s10579-018-9432-0 es_ES
dc.description.references Reitan, Johan, Jørgen Faret, Björn Gambäck, and Lars Bungum. 2015. "Negation scope detection for twitter sentiment analysis.". https://doi.org/10.18653/v1/W15-2914 es_ES
dc.description.references Schulder, Marc, Michael Wiegand, and Josef Ruppenhofer. 2020. "Enhancing a lexicon of polarity shifters through the supervised classification of shifting directions." Proceedings of the 12th International Conference on Language Resources and Evaluation (Lrec 2020): 5010-6. doi: 10.5281/zenodo.3545947. es_ES
dc.description.references Schulder, Marc, Michael Wiegand, and Josef Ruppenhofer. 2021. "Automatic generation of lexica for sentiment polarity shifters." Natural Language Engineering 27 (2) (MAR): 153-79. https://doi.org/10.1017/S135132492000039X es_ES
dc.description.references Sharif, Wareesa, Noor Azah Samsudin, Mustafa Mat Deris, and Rashid Naseem. 2016. "Effect of negation in sentiment analysis." : 718-723. https://doi.org/10.1109/INTECH.2016.7845119 es_ES
dc.description.references Singh, Prakash Kumar, and Sanchita Paul. 2021. "Deep learning approach for negation handling in sentiment analysis." IEEE Access 9 : 102579-92. https://doi.org/10.1109/ACCESS.2021.3095412 es_ES
dc.description.references Sintsova, Valentina, Margarita Bolívar Jiménez, and Pearl Pu. 2018. "Modeling the impact of modifiers on emotional statements." Paper presented at CICLing 2017. Lecture Notes in Computer Science: 71-89.Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-77116-8_6 es_ES
dc.description.references Strohm, Florian, and Roman Klinger. 2018. "An empirical analysis of the role of amplifiers, downtoners, and negations in emotion classification in microblogs." , Turin, Italy : 673-681. https://doi.org/10.1109/DSAA.2018.00087 es_ES
dc.description.references Taboada, Maite. 2016. "Sentiment analysis: An overview from linguistics." Annual Review of Linguistics 2 (1): 325-47. https://doi.org/10.1146/annurev-linguistics-011415-040518 es_ES
dc.description.references Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. "Lexicon-based methods for sentiment analysis." Computational Linguistics 37 (2): 267-307. https://doi.org/10.1162/COLI_a_00049 es_ES
dc.description.references Trnavac, Radoslava, and Maite Taboada. 2012. "The contribution of nonveridical rhetorical relations to evaluation in discourse." Language Sciences 34 (3): 301-18. https://doi.org/10.1016/j.langsci.2011.10.005 es_ES
dc.description.references Turney, Peter D., and Michael L. Littman. 2002. "Unsupervised learning of semantic orientation from a hundred-billion-word corpus." Technical Report, Nacional Research Council Canada, Institute for Information Technology, https://arxiv.org/abs/cs/0212012. es_ES
dc.description.references Wiegand, Michael, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010. "A survey on the role of negation in sentiment analysis." Paper presented at Proceedings of the workshop on negation and speculation in natural language processing: 60-68. es_ES
dc.description.references Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2009. "Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis." Computational Linguistics 35 (3): 399-433. https://doi.org/10.1162/coli.08-012-R1-06-90 es_ES
dc.description.references Xia, Rui, Feng Xu, Jianfei Yu, Yong Qi, and Erik Cambria. 2016. "Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis." Information Processing and Management 52 (1): 36-45. https://doi.org/10.1016/j.ipm.2015.04.003 es_ES
dc.description.references Xia, Rui, Feng Xu, Chengqing Zong, Qianmu Li, Yong Qi, and Tao Li. 2015. "Dual sentiment analysis: Considering two sides of one review." IEEE Transactions on Knowledge and Data Engineering 27 (8): 2120-2133. https://doi.org/10.1109/TKDE.2015.2407371 es_ES
dc.description.references Xu, Ge, Xiaoyan Yang, Yuanzheng Cai, Zhiqiang Ruan, Tao Wang, and Xiangwen Liao. 2020. "Extracting polarity shifting patterns from any corpus based on natural annotation." ACM Transactions on Asian and Low-Resource Language Information Processing 19 (2): 1-16. https://doi.org/10.1145/3345518 es_ES
dc.description.references Yoo, Gwanghoon, and Jeesun Nam. 2018. "A hybrid approach to sentiment analysis enhanced by sentiment lexicons and polarity shifting devices.", Miyazaki, Japan : 21-28. es_ES
dc.description.references Zhang, Xiaoqian, Shoushan Li, Guodong Zhou, and Hongxia Zhao. 2011. "Polarity shifting: Corpus construction and analysis." Paper presented at 2011 International Conference on Asian Language Processing: 272-275. https://doi.org/10.1109/IALP.2011.27 es_ES
dc.description.references Zirpe, Sayali, and Bela Joglekar. 2017. "Polarity shift detection approaches in sentiment analysis: A survey." Paper presented at 2017 International Conference on Inventive Systems and Control (ICISC): 1-5. https://doi.org/10.1109/ICISC.2017.8068737 es_ES


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

Mostrar el registro sencillo del ítem