Glossary
- NLP:
-
The acronym for natural language processing is used to indicate a field of artificial intelligence, also known as computational linguistics, concerned with the simulation of linguistic competence by computers that involve the interaction between computers and human natural languages. It addressed several challenges, like natural language understanding and natural language generation, and tasks, like the identification of named entities within a text, the analysis of morpho-syntactic structure of sentences, or the analysis of opinions and sentiment (sentiment analysis) expressed in a message
- Sentiment analysis:
-
Sentiment analysis, which is also known as opinion mining, indicates the use of NLP and text analysis techniques for detecting subjective information in user-generated contents. These techniques have been widely...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Attardi G, Basile V, Bosco C, Caselli T, Dell’Orletta F, Montemagni S, Patti V, Simi M, Sprugnoli R (2015) State of the art language technologies for Italian: the EVALITA 2014 perspective. J Intell Artif 9(1):43–61
Balahur A, Kozareva Z, Montoyo A (2009) Determining the polarity and source of opinions expressed in political debates. In: Gelbukh A (ed) Computational linguistics and intelligent text processing. Springer, Berlin, pp 468–480
Basile V, Nissim M (2013) Sentiment analysis on Italian tweets. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA 2013), Association for Computational Linguistics, Atlanta, Georgia, pp 100–107
Basile V, Bolioli A, Nissim M, Patti V, Rosso P (2014) Overview of the Evalita 2014 SENTIment POLarity classification task. In: Proceedings of EVALITA 2014. Pisa University Press, Pisa, pp 50–57
Bermingham A, Smeaton AF (2011) On using Twitter to monitor political sentiment and predict election results. In: Proceedings of the workshop on sentiment analysis where AI meets Psychology (SAAIP), IJCNLP 2011. Chiang Mai, pp 2–10
Bosco C, Patti V, Bolioli A (2013) Developing corpora for sentiment analysis: the case of irony and Senti–TUT. IEEE Intelligent Systems 28(2):55–63
Bosco C, Lai M, Patti V, Rangel Pardo FM, Rosso P (2016a) Tweeting in the debate about Catalan elections. In: Proceedings of the international workshop on Emotion and Sentiment Analysis at LREC2016, European Language Resources Association, Portoroz, Slovenia, pp 67–70
Bosco C, Lai M, Patti V, Virone D (2016b) Tweeting and being ironic in the debate about a political reform: the French annotated corpus twitter-mariagepourtous. In: Proceedings of the tenth international conference on Language Resources and Evaluation (LREC 2016). ELRA, Portoroz, pp 1619–1626
Boutet A, Kim H, Yoneki E (2012) What’s in your tweets? I know who you supported in the UK 2010 general election. In: Proceedings of the international AAAI conference on web and social media, Association for the Advancement of Artificial Intelligence, Dublin, Ireland, pp 411–414
Chiusaroli F (2012) Scritture brevi oggi. tra convenzione e sistema. In: Chiusaroli F, Zanzotto FM (eds) Scritture brevi di oggi. Università Orientale di Napoli, pp 4–44
Chung J, Mustafaraj E (2011) Can collective sentiment expressed on Twitter predict political elections? In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, San Francisco, California, pp 1770–1771
Conoscenti M (2011) The reframer: an analysis of Barack Obama’s political discourse (2004–2010). Bulzoni, Roma
Conover MD, Ratkiewicz J, Francisco M, Goncalves B, Flammini A, Menczer F (2011) Political polarization on Twitter. In: Proceedings of the fifth international AAAI conference on weblogs and social media, Association for the Advancement of Artificial Intelligence, Barcelona, Spain, pp 89–96
Cunha E, Magno G, Comarela G, Almeida V, Goncalves MA, Benevenuto F (2011) Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach. In: Proceedings of the workshop on language in social media (LSM 2011). Association for Computational Linguistics, Portland, pp 58–65
Davidov D, Tsur O, Rappoport A (2011) Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the CONLL’11. Portland, pp 107–116
Fraisse A, Paroubek P (2014a) Toward a unifying model for opinion, sentiment and emotion information extraction. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14). European Language Resources Association (ELRA), Reykjavik, pp 3881–3886
Fraisse A, Paroubek P (2014b) Twitter as a comparable corpus to build multilingual affective lexicons. In: Proceedings of the LREC’14 workshop on building and using comparable corpora. European Language Resources Association (ELRA), Reykjavik, pp 17–21
Gayo-Avello D (2012) I wanted to predict elections with Twitter and all i got was this lousy paper. CoRR abs/1204.6441. http://arxiv.org/abs/1204.6441
Ghosh A, Li G, Veale T, Rosso P, Shutova E, Reyes A, Barnden J (2015) Semeval-2015 task 11: sentiment analysis of figurative language in Twitter. In: Proceedings of the international workshop on semantic evaluation (SemEval-2015), co-located with NAACL and *SEM
Gotti F, Langlais P, Farzindar A (2014) Hashtag occurrences, layout and translation: a corpus driven analysis of tweets published by the Canadian government. In: Proceedings of ninth international conference on language resources and evaluation (LREC’14). European Language Resources Association (ELRA), Reykjavik, pp 2254–2261
Hong S, Kim SH (2016) Political polarization in Twitter: implications for the use of social media in digital governments. Government Information Quarterly, 33(4):777–782
Jungherr A (2016) Twitter use in election campaigns: a systematic literature review. Journal of Information technology and politics 13(1):72–91
Krieg-Planque A (2009) La notion de “formule en analyse” du discours. Cadre théorique et méthodologique. Presses universitaires de Franche-Comté, collection Annales littéraires
Lai M, Bosco C, Patti V, Virone D (2015) Debate on political reforms in Twitter: a hashtag-driven analysis of political polarization. In: IEEE Data Science and Advanced Analytics (DSAA 2015), Institute of Electrical and Electronic Engineers, Paris, France, pp 1–9
Lassen DS, Brown AR, Riding S (2010) Twitter: the electoral connection? Social Science Computer Review 29(4):419–436
Maynard D, Greenwood M (2014) Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: Proceedings of the ninth international conference on Language Resources and Evaluation (LREC’14). ELRA, Reykjavik, pp 4238–4243
Mohammad SM, Zhu X, Kiritchenko S, Martin J (2015) Sentiment, emotion, purpose, and style in electoral tweets. Information Processing Management 51(4):480–499
Mohammad SM, Kiritchenko S, Sobhani P, Zhu X, Cherry C (2016a) A dataset for detecting stance in tweets. In: Proceedings of the tenth international conference on Language Resources and Evaluation (LREC 2016), European Language Resources Association, Portoroz, Slovenia, pp 3945–3952
Mohammad SM, Kiritchenko S, Sobhani P, Zhu X, Cherry C (2016b) Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the international workshop on semantic evaluation, SemEval’16. Association for Computational Linguistics, San Diego, California, pp 31–41
Mohammad SM, Sobhani P, Kiritchenko S (2016c) Stance and sentiment in tweets. CoRR abs/1605.01655. http://arxiv.org/abs/1605.01655
Nissim M, Patti V (2017) Semantic aspects in sentiment analysis, Chapter 3. In: Fersini E, Liu B, Messina E, Pozzi F (eds) Sentiment analysis in social networks. Elsevier, Cambridge MA, pp 31–48
Pontiki M, Galanis D, Papageorgiou H, Manandhar SI (2015) Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, Colorado, pp 486–495. http://www.aclweb.org/anthology/S15-2082
Ranade S, Sangal R, Mamidi R (2013) Stance classification in online debates by recognizing users’ intentions. In: Proceedings of the SIGDIAL 2013 conference. Association for Computational Linguistics, Metz, pp 61–69
Reyes A, Rosso P, Veale T (2013) A multidimensional approach for detecting irony in Twitter. Language Resources Evaluation 47(1):239–268
Sang ETK, Bos J (2012) Predicting the 2011 Dutch senate election results with Twitter. In: Proceedings of the workshop on semantic analysis in social media. Association for Computational Linguistics, Stroudsburg, pp 53–60
Schumacher E, Eskenazi M (2016) A readability analysis of campaign speeches from the 2016 US presidential campaign. CoRR abs/1603.05739. http://arxiv.org/abs/1603.05739
Silva L, Mondal M, Correa D, Benevenuto F, Weber I (2016) Analyzing the targets of hate in online social media. In: Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016), Association for Advancements on Artificial Intelligence, Cologne, Germany, pp 687–690
Skilters J, Kreile M, Bojars U, Brikse I, Pencis J, Uzule L (2011) The pragmatics of political messages in Twitter communication. In: Garcia-Castro R, Fensel D, Antoniou G (eds) ESWC workshops. Lecture notes in computer science, vol 7117. Springer, Berlin, Germany, pp 100–111
Somasundaran S, Wiebe J (2010) Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text. Association for Computational Linguistics, Los Angeles, pp 116–124
Sridhar D, Getoor L, Walker M (2014) Collective stance classification of posts in online debate forums. In: Proceedings of the ACL joint workshop on social dynamics and personal attributes in social media. Association for Computational Linguistics, Baltimore, pp 109–117
Stranisci M, Bosco C, Hernandes Farìas DI, Patti V (2016) Annotating sentiment and irony in the online Italian political debate on #labuonascuola. In: Proceedings of the tenth international conference on language resources and evaluation (LREC 2016). European Language Resources Association, Portoroz, Slovenia, pp 2892–2899
Theocharis Y, Lowe W (2016) Does Facebook increase political participation? Evidence from a field experiment. Information, Communication & Society 19(10):1465–1486
Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the fourth international AAAI conference on weblogs and social media. Association for Advancement on Artificial Intelligence, Washington D.C., pp 178–185
Wallace BC, Choe DK, Charniak E (2015) Sparse, contextually informed models for irony detection: exploiting user communities, entities and sentiment. In: Proceedings of the 53rd annual meeting of the ACL and the 7th international joint conference on natural language processing of the Asian Federation of Natural Language Processing, Association for Computational Linguistics, Beijing, China, pp 1035–1044
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Bosco, C., Patti, V. (2018). Social Media Analysis for Monitoring Political Sentiment. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_110172
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_110172
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering