On the Processing and Analysis of Microtexts: From Normalization to Semantics

Trátase dun resumo estendido da ponencia[Abstract] User-generated content published on microblogging social platforms constitutes an invaluable source of information for diverse purposes: health surveillance, business intelligence, political analysis, etc. We present an overview of our work on the field of microtext processing covering the entire pipeline: from input preprocessing to high-level text mining applications.Ministerio de Economía Industria y Competitividad; FFI2014-51978-C2-2-RMinisterio de Economía Industria y Competitividad; TIN2017–85160–C2–1-RMinisterio de Economía Industria y Competitividad; TIN2017–85160–C2–2-RMinisterio de Economía Industria y Competitividad; BES-2015-073768Ministerio de Economía Industria y Competitividad; FFI2014-51978-C2-1-RXunta de Galicia; ED431D 2017/1

Political candidates in infotainment programmes and their emotional effects on Twitter: An analysis of the 2015 Spanish general elections pre-campaign season

This is an Accepted Manuscript of an article published by Taylor & Francis in Contemporary Social Science on 2019, available online: http://www.tandfonline.com/10.1080/21582041.2017.1367833.[EN] The infotainment format offers candidates an informal setting to show a more personal side of themselves to the electorate, opening themselves up to potential voters. An example of media hybridisation, social networks users can immediately comment on infotainment television programmes, a process known as second screening. These second screeners tend to be especially active in politics. This paper analyses the immediate emotional reaction of these users as they watch infotainment programmes that air during the campaign or pre-campaign seasons and feature political candidates as guests. We have confirmed that second screeners react more emotionally towards the candidate when his or her party is mentioned, and less emotionally when the host displays an aggressive attitude through his or her non-verbal communication. When issues related to the candidate¿s personal lives are discussed, users¿ emotional reactions improve slightly. The relevance of this research stems from the fact that we are witnessing the consolidation of a politics that increasingly strays from ideological questions, and instead focuses on more emotional and personal issues.This work was supported by the Ministerio de Economia y Competitividad under Grants CSO2013-43960-R and CSO2016-77331-C2-1-R.Baviera, T.; Peris, À.; Cano-Orón, L. (2019). Political candidates in infotainment programmes and their emotional effects on Twitter: An analysis of the 2015 Spanish general elections pre-campaign season. Contemporary Social Science. 14(1):144-156. https://doi.org/10.1080/21582041.2017.1367833S144156141Baum, M. A., & Jamison, A. S. (2006). TheOprahEffect: How Soft News Helps Inattentive Citizens Vote Consistently. The Journal of Politics, 68(4), 946-959. doi:10.1111/j.1468-2508.2006.00482.xBravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99. doi:10.1016/j.knosys.2014.05.016Casero-Ripollés, A., Feenstra, R. A., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign. The International Journal of Press/Politics, 21(3), 378-397. doi:10.1177/1940161216645340Ceron, A., & Splendore, S. (2016). From contents to comments: Social TV and perceived pluralism in political talk shows. New Media & Society, 20(2), 659-675. doi:10.1177/1461444816668187Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608Giglietto, F., & Selva, D. (2014). Second Screen and Participation: A Content Analysis on a Full Season Dataset of Tweets. Journal of Communication, 64(2), 260-277. doi:10.1111/jcom.12085Grabe, M. E., & Bucy, E. P. (2009). Image Bite Politics. doi:10.1093/acprof:oso/9780195372076.001.0001Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557-576. doi:10.1080/15205436.2015.1045300Harrington, S. (2008). Popular news in the 21st century Time for a new critical approach? Journalism: Theory, Practice & Criticism, 9(3), 266-284. doi:10.1177/1464884907089008López-Rico, C.-M., & Peris-Blanes, À. (2017). Agenda e imagen de los candidatos de las elecciones generales de 2015 en España en programas televisivos de infoentretenimiento. El Profesional de la Información, 26(4), 611. doi:10.3145/epi.2017.jul.05Maruyama, M., Robertson, S. P., Douglas, S., Raine, R., & Semaan, B. (2017). Social Watching a Civic Broadcast. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. doi:10.1145/2998181.2998340Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. doi:10.1016/j.asej.2014.04.011Saif, H., He, Y., & Alani, H. (2012). Semantic Sentiment Analysis of Twitter. Lecture Notes in Computer Science, 508-524. doi:10.1007/978-3-642-35176-1_32Shah, D. V., Hanna, A., Bucy, E. P., Lassen, D. S., Van Thomme, J., Bialik, K., … Pevehouse, J. C. W. (2016). Dual Screening During Presidential Debates. American Behavioral Scientist, 60(14), 1816-1843. doi:10.1177/0002764216676245Sullivan, D. G., & Masters, R. D. (1988). «Happy Warriors»: Leaders’ Facial Displays, Viewers’ Emotions, and Political Support. American Journal of Political Science, 32(2), 345. doi:10.2307/2111127Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society, 19(10), 1390-1410. doi:10.1080/1369118x.2015.1093526Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. doi:10.1177/0165551515598926Wohn, D. Y., & Na, E.-K. (2011). Tweeting about TV: Sharing television viewing experiences via social media message streams. First Monday. doi:10.5210/fm.v16i3.336

Public Opinion on National Exam Policies in Indonesia

Abstract Every new policy by Indonesian government in National Examination (NE) implementation always obtains different respond from public. Since the implementation, NE system already experienced many changes, but in recent years this system receives serious critiques. As a result, government then abolished this system as graduation determinant in 2014. This research analyzes public opinion, in the form of positive and negative sentiment toward NE policy, and factors that drive the opinions. Data in this research obtained from online news media from 2012 to 2015. The result shows that public sentiment fluctuating from year to year and depends on three important factors, i.e. political pressure, extreme events, and media coverage

On the performance of phonetic algorithms in microtext normalization

User-generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non-standard microtexts into standard well-written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non-standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so-called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non-standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization system. KEYWORDS: microtext normalization; phonetic algorithm; fuzzy matching; Twitter; textingComment: Accepted for publication in journal Expert Systems with Application

On the performance of phonetic algorithms in microtext normalization

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Doval, Y., Vilares, M. and Vilares, J. (2018) ‘On the performance of phonetic algorithms in microtext normalization’ has been accepted for publication in: Expert Systems with Applications, 113, pp. 213–222. The Version of Record is available online at: https://doi.org/10.1016/j.eswa.2018.07.016[Abstract]: User–generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non–standard microtexts into standard well–written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non–standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so–called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non–standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization system.This research has been partially funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through projects TIN2017-85160-C2-1-R, TIN2017-85160-C2-2-R, FFI2014-51978-C2-1-R and FFI2014-51978-C2-2-R, and by the Autonomous Government of Galicia through projects ED431D-2017/12, ED431B-2017/01 and ED431D R2016/046. Moreover, Yerai Doval is funded by the Spanish State Secretariat for Research, Development and Innovation (which belongs to MINECO) and by the European Social Fund (ESF) under a FPI fellowship (BES-2015-073768) associated to project FFI2014-51978-C2-1-R.Xunta de Galicia; ED431D-2017/12Xunta de Galicia; ED431B-2017/01Xunta de Galicia; ED431D R2016/04

On the performance of phonetic algorithms in microtext normalization

User–generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non–standard microtexts into standard well–written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non–standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so–called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non–standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization systemAgencia Estatal de Investigación | Ref. TIN2017-85160-C2-1-RAgencia Estatal de Investigación | Ref. TIN2017-85160-C2-2-RMinisterio de Economía y Competitividad | Ref. FFI2014-51978-C2-1-RMinisterio de Economía y Competitividad | Ref. FFI2014-51978-C2-2-RXunta de Galicia | Ref. ED431D-2017/12Xunta de Galicia | Ref. ED431B2017/01Xunta de Galicia | Ref. ED431D R2016/046Ministerio de Economía y Competitividad | Ref. BES-2015-07376

Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections

"This is an Accepted Manuscript of an article published by Taylor & Francis in The Communication Review on 2019, available online: https://www.tandfonline.com/doi/full/10.1080/10714421.2019.1599642"[EN] During election campaigns, candidates, parties, and media share their relevance on Twitter with a group of especially active users, aligned with a particular party. This paper introduces the profile of ¿party evangelists,¿ and explores the activity and effects these users had on the general political conversation during the 2015 Spanish general election. On that occasion, the electoral expectations were uncertain for the two major parties (PP and PSOE) because of the rise of two emerging parties that were disrupting the political status quo (Podemos and Ciudadanos). This was an ideal situation to assess the differences between the evangelists of established and emerging parties. The paper evaluates two aspects of the political conversation based on a corpus of 8.9 million tweets: the retweet- ing effectiveness, and the sentiment analysis of the overall conver- sation. We found that one of the emerging party¿s evangelists dominated message dissemination to a much greater extent.The present research was supported by the Ministerio de Economia y Competitividad [CSO2013-43960-R] [CSO2016-77331-C2-1-R]. The present research was supported by the Ministerio de Economia y Competitividad, Spain, under Grants CSO2013-43960-R ("2015-2016 Spanish political parties' online campaign strategies") and CSO2016-77331-C2-1-R ("Strategies, agendas and discourse in electoral cybercampaigns: media and citizens"). This work was possible thanks to help received from Emilio Giner in his task of extracting the corpus of tweets and from assistance provided by Mike Thelwall and David Vilares in the use of the SentiStrength application. We have benefited from valuable comments on drafts of this article from professors Joaquín Aldás, Amparo Baviera-Puig, Guillermo López-García, and especially Lidia Valera-Ordaz.Baviera, T.; Sampietro, A.; García-Ull, FJ. (2019). Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections. The Communication Review. 22(2):117-138. https://doi.org/10.1080/10714421.2019.1599642S117138222Alvarez, R., Garcia, D., Moreno, Y., & Schweitzer, F. (2015). Sentiment cascades in the 15M movement. EPJ Data Science, 4(1). doi:10.1140/epjds/s13688-015-0042-4Anduiza, E., Cristancho, C., & Sabucedo, J. M. (2013). Mobilization through online social networks: the political protest of theindignadosin Spain. Information, Communication & Society, 17(6), 750-764. doi:10.1080/1369118x.2013.808360Anstead, N., & O’Loughlin, B. (2011). The Emerging Viewertariat and BBC Question Time. 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Supervised sentiment analysis in multilingual environments

© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Vilares, D., Alonso, M.A. and Gómez-Rodríguez, C. (2017) ‘Supervised sentiment analysis in multilingual environments’ has been accepted for publication in Information Processing & Management, 53(3), pp. 595–607. The Version of Record is available online at https://doi.org/10.1016/j.ipm.2017.01.004.[Abstract]: This article tackles the problem of performing multilingual polarity classification on Twitter, comparing three techniques: (1) a multilingual model trained on a multilingual dataset, obtained by fusing existing monolingual resources, that does not need any language recognition step, (2) a dual monolingual model with perfect language detection on monolingual texts and (3) a monolingual model that acts based on the decision provided by a language identification tool. The techniques were evaluated on monolingual, synthetic multilingual and code-switching corpora of English and Spanish tweets. In the latter case we introduce the first code-switching Twitter corpus with sentiment labels. The samples are labelled according to two well-known criteria used for this purpose: the SentiStrength scale and a trinary scale (positive, neutral and negative categories). The experimental results show the robustness of the multilingual approach (1) and also that it outperforms the monolingual models on some monolingual datasets.This research was supported by the Ministerio de Economía y Competitividad (FFI2014-51978-C2) and Xunta de Galicia (R2014/034). David Vilares is funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180). Carlos Gómez-Rodríguez is funded by an Oportunius program grant (Xunta de Galicia).Xunta de Galicia; R2014/03