{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:57:17Z","timestamp":1743101837452,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030608866"},{"type":"electronic","value":"9783030608873"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60887-3_15","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T23:04:50Z","timestamp":1602025490000},"page":"167-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating Pre-trained Word Embeddings and Neural Network Architectures for Sentiment Analysis in Spanish Financial Tweets"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3651-2660","authenticated-orcid":false,"given":"Jos\u00e9 Antonio","family":"Garc\u00eda-D\u00edaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4059-9516","authenticated-orcid":false,"given":"Oscar","family":"Apolinario-Arzube","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2457-1791","authenticated-orcid":false,"given":"Rafael","family":"Valencia-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"15_CR1","unstructured":"Zaglia, M.E.: Brand communities embedded in social networks. J. Bus. Res. 66(2), 216\u2013223 (2013). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S014829631200210X"},{"key":"15_CR2","unstructured":"Conneau, A., Kruszewski, G., Lample, G., Barrault, L., Baroni, M.: What you can cram into a single vector: probing sentence embeddings for linguistic properties. CoRR abs\/1805.01070 (2018). http:\/\/arxiv.org\/abs\/1805.01070"},{"key":"15_CR3","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. CoRR abs\/1804.07461 (2018). http:\/\/arxiv.org\/abs\/1804.07461"},{"key":"15_CR4","unstructured":"Bakshi, R.K., Kaur, N., Kaur, R., Kaur, G.: Opinion mining and sentiment analysis. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 452\u2013455. IEEE (2016)"},{"issue":"3","key":"15_CR5","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TKDE.2015.2485209","volume":"28","author":"K Schouten","year":"2015","unstructured":"Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813\u2013830 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR6","unstructured":"Kolchyna, O., Souza, T.T.P., Treleaven, P.C., Aste, T.: Twitter sentiment analysis: Lexicon method, machine learning method and their combination. CoRR abs\/1507.00955 (2015). http:\/\/arxiv.org\/abs\/1507.00955"},{"key":"15_CR7","unstructured":"Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Lrec, vol. 10, pp. 2200\u20132204 (2010)"},{"key":"15_CR8","unstructured":"Ruiz-Mart\u00ednez, J.M., Valencia-Garc\u00eda, R., Garc\u00eda-S\u00e1nchez, F., et al.: Semantic-based sentiment analysis in financial news. In: Proceedings of the 1st International Workshop on Finance and Economics on the Semantic Web, pp. 38\u201351 (2012)"},{"issue":"1","key":"15_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0261927X09351676","volume":"29","author":"YR Tausczik","year":"2010","unstructured":"Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: liwc and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24\u201354 (2010)","journal-title":"J. Lang. Soc. Psychol."},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"del Pilar Salas-Z\u00e1rate, M., Paredes-Valverde, M.A., Rodriguez-Garc\u00eda, M.\u00c1., Valencia-Garc\u00eda, R., Alor-Hern\u00e1ndez, G.: Automatic detection of satire in twitter: a psycholinguistic-based approach. Knowl. Based Syst. 128, 20\u201333 (2017). https:\/\/doi.org\/10.1016\/j.knosys.2017.04.009","DOI":"10.1016\/j.knosys.2017.04.009"},{"key":"15_CR11","unstructured":"Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Standford University, CS229 15 (2012)"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Rao, T., Srivastava, S.: Analyzing stock market movements using twitter sentiment analysis. In: ASONAM 2012, pp. 119\u2013123 (2012). https:\/\/doi.org\/10.1109\/ASONAM.2012.30","DOI":"10.1109\/ASONAM.2012.30"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.eswa.2019.06.014","volume":"135","author":"A Picasso","year":"2019","unstructured":"Picasso, A., Merello, S., Ma, Y., Oneto, L., Cambria, E.: Technical analysis and sentiment embeddings for market trend prediction. Expert Syst. Appl. 135, 60\u201370 (2019)","journal-title":"Expert Syst. Appl."},{"key":"15_CR14","first-page":"1320","volume":"10","author":"A Pak","year":"2010","unstructured":"Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. LREc 10, 1320\u20131326 (2010)","journal-title":"LREc"},{"issue":"16","key":"15_CR15","doi-asserted-by":"publisher","first-page":"6266","DOI":"10.1016\/j.eswa.2013.05.057","volume":"40","author":"M Ghiassi","year":"2013","unstructured":"Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266\u20136282 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"15_CR16","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.jfds.2017.11.002","volume":"4","author":"TM Nisar","year":"2018","unstructured":"Nisar, T.M., Yeung, M.: Twitter as a tool for forecasting stock market movements: a short-window event study. J. Financ. Data Sci. 4(2), 101\u2013119 (2018)","journal-title":"J. Financ. Data Sci."},{"issue":"3","key":"15_CR17","first-page":"411","volume":"30","author":"K Krippendorff","year":"2004","unstructured":"Krippendorff, K.: Reliability in content analysis: some common misconceptions and recommendations. Hum. Commun. Res. 30(3), 411\u2013433 (2004)","journal-title":"Hum. Commun. Res."},{"key":"15_CR18","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-319-77703-0_31","volume-title":"Trends and Advances in Information Systems and Technologies","author":"JA Garc\u00eda-D\u00edaz","year":"2018","unstructured":"Garc\u00eda-D\u00edaz, J.A., Salas-Z\u00e1rate, M.P., Hern\u00e1ndez-Alcaraz, M.L., Valencia-Garc\u00eda, R., G\u00f3mez-Berb\u00eds, J.M.: Machine learning based sentiment analysis on Spanish financial Tweets. In: Rocha, \u00c1., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2018. AISC, vol. 745, pp. 305\u2013311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-77703-0_31"},{"key":"15_CR19","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"15_CR21","unstructured":"Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893 (2018)"},{"key":"15_CR22","unstructured":"Ca\u00f1ete, J.: Compilation of large Spanish unannotated corpora. https:\/\/doi.org\/10.5281\/zenodo.3247731 . Accessed 24 Aug 2020"},{"key":"15_CR23","unstructured":"Cardellino, C.: Spanish Billion Words Corpus and Embeddings. https:\/\/crscardellino.github.io\/SBWCE\/ . Accessed 24 Aug 2020"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-D\u00edaz, J.A., C\u00e1novas-Garc\u00eda, M., Valencia-Garc\u00eda, R.: Ontology-driven aspect-based sentiment analysis classification: an infodemiological case study regarding infectious diseases in latin America. Future Gener. Comput. Syst. 112, 614\u2013657 (2020). https:\/\/doi.org\/10.1016\/j.future.2020.06.019","DOI":"10.1016\/j.future.2020.06.019"},{"key":"15_CR25","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D Nonlinear Phenom. 404, 132306 (2020). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167278919305974"},{"key":"15_CR26","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https:\/\/www.tensorflow.org\/ . Software available from tensorflow.org"},{"key":"15_CR27","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"15_CR28","unstructured":"Autonomio talos [computer software] (2019). http:\/\/github.com\/autonomio\/talos"},{"key":"15_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"15_CR30","unstructured":"Patel, N., Upadhyay, S.: Study of various decision tree pruning methods with their empirical comparison in weka. Int. J. Comput. Appl. 60(12), 20\u201325 (2012)"},{"key":"15_CR31","unstructured":"Yin, W., Kann, K., Yu, M., Sch\u00fctze, H.: Comparative study of CNN and RNN for natural language processing. CoRR abs\/1702.01923 (2017). http:\/\/arxiv.org\/abs\/1702.01923"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning based text classification: a comprehensive review. arXiv preprint arXiv:2004.03705 (2020)","DOI":"10.1145\/3439726"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"Onan, A.: Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr. Comput. Pract. Exp. e5909 (2020)","DOI":"10.1002\/cpe.5909"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60887-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T05:02:12Z","timestamp":1696827732000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60887-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030608866","9783030608873"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60887-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"186","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}