Sentiment analysis methods for politics and hate speech contents in Spanish language: a systematic review
Keywords:
hate speech, machine learning, opinion mining, politics, sentiment analysis, twitterAbstract
The political debate in social networks, and its derivatives such as hate speech, has surfaced at the top of the social agenda due to its impact on public opinion and, consequently, in the communication strategies of political parties, public institutions, media corporations, and lobbies. The scientific community has been working to respond to the demand for tools that allow studying the political attitude of citizens in these networks, focusing on sentiment analysis methodologies. However, their work has been hampered by several significant challenges, such as the absence of standardized investigation methodologies, the filtering of content created by bots and spammers, or the interpretation of slang and other conventionalisms that are specific to microblogging platforms. In addition to these challenges and the generic problems related to the interpretation of human language, researchers from the Spanish-speaking community have found themselves with the additional problem of developing strategies and methodologies suitable for Spanish text, in a scenario dominated by research aimed at the English language. In this paper, we present a systematic review that describes the state of the art in sentiment analysis methods for politics and hate speech contents in the Spanish language, by systematically reviewing the relevant papers available.
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J. C. Pereira-Kohatsu, L. Quijano-Sánchez, F. Liberatore, and M. Camacho-Collados, “Detecting and monitoring hate speech in twitter,” Sensors (Switzerland), vol. 19, no. 21, Nov. 2019, doi: 10.3390/s19214654.
B. Saberi and S. Saad, “Sentiment Analysis or Opinion Mining: A Review,” vol. 7, no. 5, 2018, doi: 10.18517/ijaseit.7.5.2137.
X. Fang and J. Zhan, “Sentiment analysis using product review data,” J. Big Data, vol. 2, no. 1, 2015, doi: 10.1186/s40537-015-0015-2.
A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, “Current state of text sentiment analysis from opinion to emotion mining,” ACM Comput. Surv., vol. 50, no. 2, May 2017, doi: 10.1145/3057270.
F. N. Ribeiro, M. Araújo, P. Gonçalves, M. A. Gonçalves, and F. Benevenuto, “SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods,” 2016, doi: 10.1140/epjds/s13688-016-0085-1.
H. Peng, E. Cambria, and A. Hussain, “A Review of Sentiment Analysis Research in Chinese Language,” Cognit. Comput., vol. 9, no. 4, pp. 423–435, 2017, doi: 10.1007/s12559-017-9470-8.
H. C. Teo, A. Campos-Arceiz, B. V. Li, M. Wu, and A. M. Lechner, “Building a green Belt and Road: A systematic review and comparative assessment of the Chinese and English-language literature,” PLoS One, vol. 15, no. 9 September, 2020, doi: 10.1371/journal.pone.0239009.
C. H. Chen, P. Y. Chen, and J. C. W. Lin, “An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators,” Int. J. Interact. Multimed. Artif. Intell., vol. 7, no. 3, pp. 53–64, 2022, doi: 10.9781/ijimai.2022.02.004.
N. Boudad, R. Faizi, R. Oulad Haj Thami, and R. Chiheb, “Sentiment analysis in Arabic: A review of the literature,” Ain Shams Eng. J., vol. 9, no. 4, pp. 2479–2490, 2018, doi: 10.1016/j.asej.2017.04.007.
A. Ghallab, A. Mohsen, and Y. Ali, “Arabic Sentiment Analysis: A Systematic Literature Review,” Appl. Comput. Intell. Soft Comput., vol. 2020, 2020, doi: 10.1155/2020/7403128.
M. V. Mäntylä, D. Graziotin, and M. Kuutila, “The evolution of sentiment analysis—A review of research topics, venues, and top cited papers,” Computer Science Review, vol. 27. Elsevier Ireland Ltd, pp. 16–32, 2018, doi: 10.1016/j.cosrev.2017.10.002.
J. Schuster, H. Jörgens, and N. Kolleck, “The rise of global policy networks in education: analyzing Twitter debates on inclusive education using social network analysis,” J. Educ. Policy, vol. 36, no. 2, pp. 211–231, 2021, doi: 10.1080/02680939.2019.1664768.
J. Supovitz, “Social media is the new player in the politics of education,” Phi Delta Kappan, vol. 99, no. 3, pp. 50–55, 2017, doi: 10.1177/0031721717739594.
P. L. Pérez Díaz, C. Berná Sicilia, and E. Arroyas Langa, “The conversation on political issues on Twitter: An analysis of the participation and frames in the debate on the ‘wert Law’ and evictions in Spain,” Obets, vol. 11, no. 1, pp. 311–330, 2016, doi: 10.14198/OBETS2016.11.1.12.
M. J. Petrilli, “What twitter says about the education policy debate: And how scholars might use it as a research tool,” Educ. Next, vol. 15, no. 4, pp. 78–79, 2015.
F. Mirzaalian and E. Halpenny, “Social media analytics in hospitality and tourism: A systematic literature review and future trends,” J. Hosp. Tour. Technol., vol. 10, no. 4, pp. 764–790, 2019, doi: 10.1108/JHTT-08-2018-0078.
A. Reyes-Menendez, J. R. Saura, and F. Filipe, “The importance of behavioral data to identify online fake reviews for tourism businesses: A systematic review,” PeerJ Comput. Sci., vol. 2019, no. 9, 2019, doi: 10.7717/peerj-cs.219.
A. Valdivia et al., “Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods,” Neurocomputing, vol. 353, pp. 3–16, 2019, doi: 10.1016/j.neucom.2018.09.096.
F. Magami and L. A. Digiampietri, “Automatic detection of depression from text data: A systematic literacture review,” ACM Int. Conf. Proceeding Ser., 2020, doi: 10.1145/3411564.3411603.
F. J. Ramírez-Tinoco, G. Alor-Hernández, J. L. Sánchez-Cervantes, M. del P. Salas-Zárate, and R. Valencia-García, “Use of sentiment analysis techniques in healthcare domain,” Stud. Comput. Intell., vol. 815, pp. 189–212, 2019, doi: 10.1007/978-3-030-06149-4_8.
S. Hakak, W. Z. Khan, S. Bhattacharya, G. T. Reddy, and K. K. R. Choo, “Propagation of Fake News on Social Media: Challenges and Opportunities,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12575 LNCS, pp. 345–353, 2020, doi: 10.1007/978-3-030-66046-8_28.
S. Hakak, M. Alazab, S. Khan, T. R. Gadekallu, P. K. R. Maddikunta, and W. Z. Khan, “An ensemble machine learning approach through effective feature extraction to classify fake news,” Futur. Gener. Comput. Syst., vol. 117, pp. 47–58, 2021, doi: 10.1016/j.future.2020.11.022.
A. Bakliwal, J. Foster, J. van der Puil, R. O’Brien, L. Tounsi, and M. Hughes, “Sentiment Analysis of Political Tweets: Towards an Accurate Classifier,” Proc. Work. Lang. Anal. Soc. Media, no. Lasm, pp. 49–58, 2013, [Online]. Available: http://www.aclweb.org/anthology/W13-1106.
G. G. Esparza et al., “A sentiment analysis model to analyze students reviews of teacher performance using support vector machines,” Adv. Intell. Syst. Comput., vol. 620, pp. 157–164, 2018, doi: 10.1007/978-3-319-62410-5_19.
M. B. López, G. Alor-Hernández, J. L. Sánchez-Cervantes, and M. D. P. Salas-Zárate, “EduRP: An educational resources platform based on opinion mining and semantic web,” J. Univers. Comput. Sci., vol. 24, no. 11, pp. 1515–1535, 2018.
M. P. Ortega, L. B. Mendoza, J. M. Hormaza, and S. V. Soto, “Accuracy’ measures of sentiment analysis algorithms for spanish corpus generated in peer assessment,” ACM Int. Conf. Proceeding Ser., 2020, doi: 10.1145/3410352.3410838.
J. A. García-Díaz, M. Cánovas-García, and R. Valencia-García, “Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America,” Futur. Gener. Comput. Syst., vol. 112, pp. 641–657, 2020, doi: 10.1016/j.future.2020.06.019.
S. M. Jiménez-Zafra, M. T. Martín-Valdivia, I. Maks, and R. Izquierdo, “Analysis of patient satisfaction in Dutch and Spanish online reviews,” Proces. Leng. Nat., vol. 58, pp. 101–108, 2017.
C. de las Heras-Pedrosa, P. Sánchez-Núñez, and J. I. Peláez, “Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems,” Int. J. Environ. Res. Public Health, vol. 17, no. 15, pp. 1–22, 2020, doi: 10.3390/ijerph17155542.
M. Ferrer-Serrano, M. P. Latorre-Martínez, and R. Lozano-Blasco, “Universities and communication: Role of twitter during the beginning of the covid-19 health crisis,” Prof. la Inf., vol. 29, no. 6, pp. 1–18, 2020, doi: 10.3145/epi.2020.nov.12.
J. Cabezas, D. Moctezuma, A. Fernández-Isabel, and I. M. de Diego, “Detecting emotional evolution on twitter during the COVID-19 pandemic using text analysis,” Int. J. Environ. Res. Public Health, vol. 18, no. 13, 2021, doi: 10.3390/ijerph18136981.
B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” Keele, UK, 2007.
D. Moher et al., “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” PLoS Med., vol. 6, no. 7, 2009, doi: 10.1371/journal.pmed.1000097.
I. Cervantes, CVC. El español en el mundo. Anuario del Instituto Cervantes 2018. 2018.
Scopus, “Scopus,” SCOPUS - Available online. https://www.scopus.com/ (accessed Jun. 01, 2022).
“Web of Science,” “Web of Science,” Available online. https://idp.fecyt.es/ (accessed Jun. 01, 2022).
Á. Cuesta, D. F. Barrero, and M. D. R-Moreno, “A framework for massive twitter data extraction and analysis,” Malaysian J. Comput. Sci., vol. 27, no. 1, pp. 50–67, 2014.
F. Pla and L. F. Hurtado, “Political tendency identification in twitter using sentiment analysis techniques,” in COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers, 2014, pp. 183–192, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959920717&partnerID=40&md5=73d61dd37e2a1b4c86dd14ed33bc2d46.
F. Agulló, A. Guillén, Y. Gutiérrez, and P. Martínez-Barco, “ElectionMap: Una representación geolocalizada de intenciones de voto hacia partidos políticos sobre la base de comentarios de usuarios de Twitter,” Proces. Leng. Nat., vol. 55, pp. 195–198, 2015.
D. Vilares, M. Thelwall, and M. A. Alonso, “The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets,” J. Inf. Sci., vol. 41, no. 6, pp. 799–813, 2015, doi: 10.1177/0165551515598926.
J. A. Cerón-Guzmán and E. León-Guzmán, “A sentiment analysis system of Spanish tweets and its application in Colombia 2014 presidential election,” Proc. - 2016 IEEE Int. Conf. Big Data Cloud Comput. BDCloud 2016, Soc. Comput. Networking, Soc. 2016 Sustain. Comput. Commun. Sustain. 2016, pp. 250–257, 2016, doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.47.
R. Castro, L. Kuffó, and C. Vaca, “Back to #6D: Predicting Venezuelan states political election results through Twitter,” 2017 4th Int. Conf. eDemocracy eGovernment, ICEDEG 2017, pp. 148–153, 2017, doi: 10.1109/ICEDEG.2017.7962525.
P. Singh, R. S. Sawhney, and K. S. Kahlon, “Predicting the outcome of spanish general elections 2016 using twitter as a tool,” Commun. Comput. Inf. Sci., vol. 712, pp. 73–83, 2017, doi: 10.1007/978-981-10-5780-9_7.
C. Arcila-Calderón, F. Ortega-Mohedano, J. Jiménez-Amores, and S. Trullenque, “Supervised sentiment analysis of political messages in Spanish: Real-time classification of tweets based on machine learning,” Prof. la Inf., vol. 26, no. 5, pp. 973–982, 2017, doi: 10.3145/epi.2017.sep.18.
E. Gómez-Torres, R. Jaimes, O. Hidalgo, and S. Luján-Mora, “Influencia de redes sociales en el análisis de sentimiento aplicado a la situación política en Ecuador (Influence of social networks on the analysis of sentiment applied to the political situation in Ecuador),” Enfoque UTE, vol. 1, no. 1, pp. 67–78, 2018, [Online]. Available: http://ingenieria.ute.edu.ec/enfoqueute/index.php/revista/article/view/235.
J. I. Criado and J. Villodre, “Local public sector big data communication on social media. A sentiment analysis in Twitter | Comunicando datos masivos del sector público local en redes sociales. Análisis de sentimiento en twitter,” Prof. la Inf., vol. 27, no. 3, pp. 614–623, 2018.
O. Hidalgo, R. Jaimes, E. Gomez, and S. Lujan-Mora, “Sentiment analysis applied to the popularity level of the ecuadorian political leader rafael correa,” Proc. - 2017 Int. Conf. Inf. Syst. Comput. Sci. INCISCOS 2017, vol. 2017-Novem, pp. 340–346, 2018, doi: 10.1109/INCISCOS.2017.64.
V. D. G. Vera, L. M. M. Suárez, and I. C. P. Lopera, “Sentiment Analysis on Post conflict in Colombia: A Text Mining Approach,” Asian J. Appl. Sci. (ISSN 2321--0893), vol. 6, no. 02, pp. 53–59, 2018.
J. M. Pérez and F. M. Luque, “Atalaya at SemEval 2019 task 5: Robust embeddings for tweet classification,” NAACL HLT 2019 - Int. Work. Semant. Eval. SemEval 2019, Proc. 13th Work., pp. 64–69, 2019, doi: 10.18653/v1/s19-2008.
L. E. A. Vega, J. Reyes-Magaña, H. Gómez-Adorno, and G. Bel-Enguix, “MineriaUNAM at SemEval-2019 task 5: Detecting hate speech in Twitter using multiple features in a combinatorial framework,” NAACL HLT 2019 - Int. Work. Semant. Eval. SemEval 2019, Proc. 13th Work., pp. 447–452, 2019, doi: 10.18653/v1/s19-2079.
V. W. Bohorquez Lopez, C. M. Lazarte, L. Altube, and E. Santana, “Identification of the sentiment expressed using social networks in a political context,” in 25th Americas Conference on Information Systems, AMCIS 2019, 2019, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084021202&partnerID=40&md5=beaa8c0cf7b7648725c901e368472d5d.
J. N. Franco-Riquelme, A. Bello-Garcia, and J. Ordieres-Meré, “Indicator Proposal for Measuring Regional Political Support for the Electoral Process on Twitter: The Case of Spain’s 2015 and 2016 General Elections,” IEEE Access, vol. 7, pp. 62545–62560, 2019, doi: 10.1109/ACCESS.2019.2917398.
T. Baviera, À. Peris, and L. Cano-Orón, “Political candidates in infotainment programmes and their emotional effects on Twitter: an analysis of the 2015 Spanish general elections pre-campaign season,” Contemp. Soc. Sci., vol. 14, no. 1, pp. 144–156, 2019, doi: 10.1080/21582041.2017.1367833.
T. Baviera, A. Sampietro, and F. J. García-Ull, “Political conversations on Twitter in a disruptive scenario: The role of ‘party evangelists’ during the 2015 Spanish general elections,” Commun. Rev., vol. 22, no. 2, pp. 117–138, 2019, doi: 10.1080/10714421.2019.1599642.
S. Almatarneh, P. Gamallo, F. J. R. Pena, and A. Alexeev, “Supervised Classifiers to Identify Hate Speech on English and Spanish Tweets,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11853 LNCS, no. November, pp. 23–30, 2019, doi: 10.1007/978-3-030-34058-2_3.
A. T. Cignarella, “Exploring the role of syntax in irony and stance detection tasks,” CEUR Workshop Proc., vol. 2633, no. Sepln, pp. 26–31, 2020.
P. Sanchez-Nunez, E. R. Yanez, F. E. Cabrera, and A. Pelaez-Repiso, “Government Communication Management in Digital Ecosystems: A Real Case of Country Brand Analysis,” 2020 7th Int. Conf. eDemocracy eGovernment, ICEDEG 2020, pp. 264–268, 2020, doi: 10.1109/ICEDEG48599.2020.9096861.
D. Grimaldi, J. D. Cely, and H. Arboleda, “Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00334-5.
E. W. Pamungkas, V. Basile, and V. Patti, “Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study,” Inf. Process. Manag., vol. 57, no. 6, p. 102360, 2020, doi: 10.1016/j.ipm.2020.102360.
C. Arcila Calderón, D. Blanco-Herrero, and M. B. Valdez Apolo, “Rechazo y discurso de odio en Twitter: análisis de contenido de los tuits sobre migrantes y refugiados en español / Rejection and Hate Speech in Twitter: Content Analysis of Tweets about Migrants and Refugees in Spanish,” Rev. Española Investig. Sociológicas, no. December, pp. 21–40, 2020, doi: 10.5477/cis/reis.172.21.
M. Blasco-Duatis and G. Coenders, “Sentiment analysis of the agenda of Spanish political parties on Twitter during the 2018 motion of no confidence. A compositional data approach,” Rev. Mediterr. Comun., vol. 11, no. 2, pp. 185–198, 2020, doi: 10.14198/MEDCOM2020.11.2.22.
J. Pastor-Galindo et al., “Spotting political social bots in Twitter: A use case of the 2019 Spanish general election,” IEEE Trans. Netw. Serv. Manag., pp. 1–1, 2020, doi: 10.1109/tnsm.2020.3031573.
C. Arcila-Calderón, G. de la Vega, and D. B. Herrero, “Topic modeling and characterization of hate speech against immigrants on twitter around the emergence of a far-right party in Spain,” Soc. Sci., vol. 9, no. 11, pp. 1–19, 2020, doi: 10.3390/socsci9110188.
A. Ramón-Hernández, A. Simón-Cuevas, M. M. G. Lorenzo, L. Arco, and J. Serrano-Guerrero, “Towards context-aware opinion summarization for monitoring social impact of news,” Inf., vol. 11, no. 11, pp. 1–21, 2020, doi: 10.3390/info11110535.
F. M. Plaza-Del-Arco, M. D. Molina-Gonzalez, L. A. Urena-Lopez, and M. T. Martin-Valdivia, “A multi-task learning approach to hate speech detection leveraging sentiment analysis,” IEEE Access, vol. 9, pp. 112478–112489, 2021, doi: 10.1109/ACCESS.2021.3103697.
F. M. Plaza-del-Arco, M. D. Molina-González, L. A. Ureña-López, and M. T. Martín-Valdivia, “Comparing pre-trained language models for Spanish hate speech detection,” Expert Syst. Appl., vol. 166, no. September 2020, p. 114120, 2021, doi: 10.1016/j.eswa.2020.114120.
A. Córdoba-Cabús, M. Hidalgo-Arjona, and Á. López-Martín, “Coverage of the 2021 Madrid regional election campaign by the main Spanish newspapers on Twitter: natural language processing and machine learning algorithms,” Prof. la Inf., vol. 30, no. 6, 2021, doi: 10.3145/epi.2021.nov.11.
D. A. Andrade-Segarra and G. A. León-Paredes, “Deep Learning-based Natural Language Processing Methods Comparison for Presumptive Detection of Cyberbullying in Social Networks,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 5, pp. 796–803, 2021, doi: 10.14569/IJACSA.2021.0120592.
R. L. Tamayo, D. C. Castro, and R. O. Bueno, “Deep modeling of latent representations for twitter profiles on Hate Speech Spreaders identification task,” CEUR Workshop Proc., vol. 2936, pp. 2035–2046, 2021.
M. Uzan and Y. HaCohen-Kerner, “Detecting Hate Speech Spreaders on Twitter using LSTM and BERT in English and Spanish,” CEUR Workshop Proc., vol. 2936, pp. 2178–2185, 2021.
R. R. Romero-Vega, O. M. Cumbicus-Pineda, R. A. López-Lapo, and L. A. Neyra-Romero, “Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing,” Commun. Comput. Inf. Sci., vol. 1388 CCIS, pp. 312–326, 2021, doi: 10.1007/978-3-030-71503-8_24.
J. Sánchez-Junquera, B. Chulvi, P. Rosso, and S. P. Ponzetto, “How do you speak about immigrants? Taxonomy and stereoimmigrants dataset for identifying stereotypes about immigrants,” Appl. Sci., vol. 11, no. 8, 2021, doi: 10.3390/app11083610.
R. Jain, D. Goel, P. Sahu, A. Kumar, and J. P. . Singh, “Profiling Hate Speech Spreaders on Twitter,” CEUR Workshop Proc., vol. 2936, p. Paper 175, 2021.
L. Gómez-Zaragozá and S. H. Pinto, “Profiling Hate Speech Spreaders on Twitter using stylistic features and word embeddings,” CEUR Workshop Proc., vol. 2936, pp. 1953–1962, 2021.
A. Huertas-García, J. Huertas-Tato, A. Martín, and D. Camacho, “Profiling Hate Speech Spreaders on Twitter: Transformers and mixed pooling,” CEUR Workshop Proc., vol. 2936, p. Paper 170, 2021.
C. Arcila-Calderón, D. Blanco-Herrero, M. Frías-Vázquez, and F. Seoane, “Refugees welcome? Online hate speech and sentiments in twitter in spain during the reception of the boat aquarius,” Sustain., vol. 13, no. 5, pp. 1–17, 2021, doi: 10.3390/su13052728.
M. Rodriguez-Ibanez, F. J. Gimeno-Blanes, P. M. Cuenca-Jimenez, C. Soguero-Ruiz, and J. L. Rojo-Alvarez, “Sentiment Analysis of Political Tweets from the 2019 Spanish Elections,” IEEE Access, vol. 9, pp. 101847–101862, 2021, doi: 10.1109/ACCESS.2021.3097492.
R. Cervero, “Use of lexical and psycho-emotional information to detect Hate Speech Spreaders on Twitter,” CEUR Workshop Proc., vol. 2936, pp. 1883–1891, 2021.
P. Rendón-Cardona, J. Gil-Gonzalez, J. Páez-Valdez, and M. Rivera-Henao, “Self-Supervised Sentiment Analysis in Spanish to Understand the University Narrative of the Colombian Conflict,” Appl. Sci., vol. 12, no. 11, p. 5472, 2022, doi: 10.3390/app12115472.
J. M. Robles, J. A. Guevara, B. Casas-Mas, and D. Gömez, “When negativity is the fuel. Bots and Political Polarization in the COVID-19 debate,” Comunicar, vol. 30, no. 71, pp. 1–12, 2022, doi: 10.3916/C71-2022-05.
J. Mahmud, J. Nichols, and C. Drews, “Where is this tweet from?: Inferring home locations of Twitter users,” ICWSM 2012 - Proc. 6th Int. AAAI Conf. Weblogs Soc. Media, no. January, pp. 511–514, 2012.
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, “Sentiment in short strength detection informal text,” J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 12, pp. 2544–2558, Dec. 2010, doi: 10.1002/ASI.21416.
“AFINN,” “‘AFINN,’” Available online. https://darenr.github.io/afinn/ (accessed Jun. 01, 2022).
R. Palomino, C. Meléndez, D. Mauricio, and J. Valverde-Rebaza, “ANEW for Spanish twitter sentiment analysis using instance-based multi-label learning algorithms,” Commun. Comput. Inf. Sci., vol. 898, pp. 46–53, 2019, doi: 10.1007/978-3-030-11680-4_6.
F. Å. Nielsen, “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs,” CEUR Workshop Proc., vol. 718, pp. 93–98, 2011.
E. Martínez-Cámara, M. T. Martín-Valdivia, M. D. Molina-González, and L. A. Ureña-López, “Bilingual Experiments on an Opinion Comparable Corpus,” WASSA 2013 - 4th Work. Comput. Approaches to Subj. Sentim. Soc. Media Anal. Proc., no. June, pp. 87–93, 2013.
“LinguaKit,” Available online. https://linguakit.com/ (accessed Oct. 01, 2020).
Y. Chen and S. Skiena, “Building sentiment lexicons for all major languages,” 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 2, pp. 383–389, 2014, doi: 10.3115/v1/p14-2063.
“NLTK Project,” Available online. https://www.nltk.org/ (accessed Oct. 01, 2020).
“WordNet - A Lexical Database for English,” Princeton University - Available online. https://wordnet.princeton.edu/ (accessed Jun. 01, 2022).
“The Natural Language Processing Group at Stanford University,” Available online. https://nlp.stanford.edu/ (accessed Oct. 01, 2020).
Stanford NLP Group, “Stanford NLP - Core NLP,” Stanford NLP. https://stanfordnlp.github.io/CoreNLP/ (accessed Jun. 01, 2022).
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, no. Mlm, pp. 4171–4186, 2019.
“Freeling,” Available online. http://nlp.lsi.upc.edu/freeling (accessed Oct. 01, 2020).
B. O’Connor, M. Krieger, and D. Ahn, “TweetMotif: Exploratory search and topic summarization for Twitter,” ICWSM 2010 - Proc. 4th Int. AAAI Conf. Weblogs Soc. Media, pp. 384–385, 2010.
“Viz Tweet Sentiment Visualization,” Available online. https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ (accessed Oct. 01, 2020).
“SciKit,” Available online. https://scikit-learn.org/ (accessed Oct. 30, 2020).
F. Pedregosa FABIANPEDREGOSA et al., “Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011, Accessed: Jul. 17, 2022. [Online]. Available: http://scikit-learn.sourceforge.net.
“Tweepy,” “Tweepy,” www.tweepy.org - available online. https://www.tweepy.org/ (accessed Jun. 01, 2022).
J. Pastor-Galindo et al., “Twitter social bots: The 2019 Spanish general election data,” Data Br., vol. 32, 2020, doi: 10.1016/j.dib.2020.106047.
A. Giachanou and F. Crestani, “Like It or Not: A Survey of Twitter Sentiment Analysis Methods,” ACM Comput. Surv., vol. 49, no. 2, pp. 1–41, Jun. 2016, doi: 10.1145/2938640.
P. T. Metaxas, E. Mustafaraj, and D. Gayo-Avello, “How (Not) to predict elections,” Proc. - 2011 IEEE Int. Conf. Privacy, Secur. Risk Trust IEEE Int. Conf. Soc. Comput. PASSAT/SocialCom 2011, pp. 165–171, 2011, doi: 10.1109/PASSAT/SocialCom.2011.98.
Statista, “Número de usuarios de Facebook en España de 2014 a 2019,” Available online. https://es.statista.com/estadisticas/518719/usuarios-de-facebook-en-espana/ (accessed Oct. 30, 2020).
