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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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Come hither or go away? Recognising pre-electoral coalition signals in the news ...
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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Word Sense Disambiguation for 158 Languages using Word Embeddings Only ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
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Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020
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A Twitter Political Corpus of the 2019 10N Spanish Election
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Abstract:
[EN] We present a corpus of Spanish tweets of 15 Twitter accounts of politicians of the main five parties (PSOE, PP, Cs, UP and VOX) covering the campaign of the Spanish election of 10th November 2019 (10N Spanish Election). We perform a semi-automatic annotation of domainspecific topics using a mixture of keyword-based and supervised techniques. In this preliminary study we extracted the tweets of few politicians of each party with the aim to analyse their official communication strategy. Moreover, we analyse sentiments and emotions employed in the tweets. Although the limited size of the Twitter corpus due to the very short time span, we hope to provide with some first insights on the communication dynamics of social network accounts of these five Spanish political parties. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). ; Sánchez-Junquera, J.; Ponzetto, SP.; Rosso, P. (2020). A Twitter Political Corpus of the 2019 10N Spanish Election. Springer. 41-49. https://doi.org/10.1007/978-3-030-58323-1_4 ; S ; 41 ; 49 ; Abercrombie, G., Nanni, F., Batista-Navarro, R., Ponzetto, S.P.: Policy preference detection in parliamentary debate motions. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 249–259. Association for Computational Linguistics, November 2019 ; Ekman, P., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712 (1987) ; Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: 2015 IEEE/ACM International Conference on ASONAM, pp. 97–104. IEEE (2015) ; Glavaš, G., Nanni, F., Ponzetto, S.P.: Computational analysis of political texts: bridging research efforts across communities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, Florence, Italy, pp. 18–23. Association for Computational Linguistics, July 2019 ; Kornilova, A., Argyle, D., Eidelman, V.: Party matters: enhancing legislative embeddings with author attributes for vote prediction, pp. 510–515. Association for Computational Linguistics, July 2018 ; Lowe, W., Benoit, K., Mikhaylov, S., Laver, M.: Scaling policy preferences from coded political texts. Legis. Stud. Q. 36(1), 123–155 (2011) ; Marchetti-Bowick, M., Chambers, N.: Learning for microblogs with distant supervision: political forecasting with Twitter. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, pp. 603–612. Association for Computational Linguistics, April 2012 ; Menini, S., Nanni, F., Ponzetto, S.P., Tonelli, S.: Topic-based agreement and disagreement in US electoral manifestos. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 2938–2944. Association for Computational Linguistics, September 2017 ; Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., Stoyanov, V.: Semeval-2016 task 4: sentiment analysis in Twitter. arXiv preprint arXiv:1912.01973 (2019) ; Nanni, F., et al.: Findings from the hackathon on understanding euroscepticism through the lens of textual data. European Language Resources Association (ELRA), May 2018 ; O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, 23–26 May 2010 (2010) ; Rheault, L., Cochrane, C.: Word embeddings for the analysis of ideological placement in parliamentary corpora. Polit. Anal. 1–22 (2019) ; Roberts, M.E., et al.: Structural topic models for open-ended survey responses. Am. J. Polit. Sci. 58(4), 1064–1082 (2014) ; Sidorov, G., et al.: Empirical study of machine learning based approach for opinion mining in tweets. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012. LNCS (LNAI), vol. 7629, pp. 1–14. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37807-2_1 ; Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61(12), 2544–2558 (2010) ; Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335. Association for Computational Linguistics, July 2006
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Keyword:
LENGUAJES Y SISTEMAS INFORMATICOS; Political text analysis; Sentiment and emotion analysis; Topic detection; Twitter
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URL: http://hdl.handle.net/10251/179804
https://doi.org/10.1007/978-3-030-58323-1_4
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AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings
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Policy preference detection in parliamentary debate motions
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Watset: Local-global graph clustering with applications in sense and frame induction
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