Natural Language Processing Applied to Big Data in Social Networks

https://doi.org/10.31810/rsel.51.2.7

Keywords:

Natural Language Processing; big data; social media; sentiment analysis.

Abstract

The advent and rise of computer mediated communication, mainly of so-called social networks, ask for automated analytical capabilities to extract information and patterns from massive poorly structured data in order to anticipate future trends, events and actions. This area attracts both researchers and industries, with Linguistics, Computer Science, Psychology, Social Sciences or Statistics involved, among others.

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References

Amiri, H. y Daume III, H. (2015): «Target-dependent churn classification in microblogs», en Proceedings of the 29th AAAI Conference on AI, pp. 2361-2367.

Androutsopoulos, J. (2013): «Online data collection», en Mallinson, C., Childs, B. y Van Herk, G. (eds.) Data Collection in Sociolinguistics: Methods and Applications, Routledge, pp. 236-249.

Argamon, S., Koppel, M., Pennebaker, J. W. y Schler, J. (2009): «Automatically profiling the author of an anonymous text», Communications of the ACM 52, pp. 119-123.

Balahur, A., Hermida, J. M., Montoyo, A. y Muñoz, R. (2011): «EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories», en Proceedings of the 16th international conference on NLP and information systems, Alicante, pp. 27-39.

Barbieri, F. (2008): «Patterns of age-based linguistic variation in American English», Journal of Sociolinguistics 12, pp. 58-88.

Bosco C., Lai M., Patti V., Rangel F. y Rosso P. (2016): «Tweeting in the debate about Catalan elections», en Proceedings of the LREC Emotion and Sentiment Analysis Workshop, Portorož, pp. 67-70.

Calvo, R. A. y D’Mello, S. (2010): «Affect detection: An interdisciplinary review of models, methods, and their applications», en IEEE Transactions on Affective Computing 1, pp. 18-37.

Chen, L., Org, C., Wang, W., Org, W., Nagarajan, M., Wang, S., Sheth, A. P. y Org, A. (2012): «Extracting diverse sentiment expressions with target-dependent polarity from Twitter», en Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublín, pp. 50-57.

Crystal, D. (2008): Txtng: The Gr8 Db8, Oxford, Oxford University Press.

Dale, R. (2017): «NLP in a post-truth world», Natural Language Engineering 23, pp. 319-324.

Danescu-Niculescu-Mizil, C., Gamon, M. y Dumais, S. (2011): «Mark my words!: Linguistic style accommodation in social media», en Proceedings of the 20th International Conference on WWW, Hyderabad, pp. 745-754.

Daniulaityte, R., Nahhas, R. W., Wijeratne, S., Carlson, R. G., Lamy, F. R., Martins, S. S., Boyer, E. W., Smith, G. A. y Sheth, A. (2015): «‘Time for dabs’: Analyzing Twitter data on marijuana concentrates across the U.S. HHS Public Access», Drug Alcohol Depend 155, pp. 307-311.

Donoso, G. y Sanchez, D. (2017): «Dialectometric analysis of language variation in Twitter», en Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pp. 16-25.

Eisenstein, J. (2013): «Phonological factors in social media writing», en Proceedings of the Workshop on Language Analysis in Social Media, Atlanta, pp. 11-19.

Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J. y Smith, N. A. (2011): «Part-of-speech tagging for Twitter: Annotation, features, and experiments», en Proceedings of the ACL-2011, Stroudsburg, pp. 42-47.

Grčar, M., Cherepnalkoski, D., Mozetič, I. y Novak, P. K. (2017): «Stance and influence of Twitter users regarding the Brexit referendum», Computational Social Networks 4.

Hermida, A. (2012): «Tweets and truth: Journalism as a discipline of collaborative verification», Journalism Practice 6, pp. 659-668.

Hong, L. y Davison, B. D. (2010): «Empirical study of topic modeling in Twitter», en Proceedings of the 1st Workshop on Social Media Analytics, Washington D.C., pp. 80-88.

Huang, B., Kechadi, M. T. y Buckley, B. (2012): «Customer churn prediction in telecommunications», Expert Systems with Applications 39, pp. 1414-1425.

Kawale, J., Pal, A. y Srivastava, J. (2009): «Churn prediction in MMORPGs: A social influence based approach», en Proceedings of International Conference on Computational Science and Engineering. IEEE Computer Society.

Klavans, L. y Resnik, P. (eds.) (1996): The balancing act: Combining symbolic and statistical approaches to language, Cambridge, MIT press.

Lai M., Hernández I., Patti V. y Rosso P. (2017): «Friends and enemies of Clinton and Trump: Using context for detecting stance in political tweets», en Proceedings of the 15th Mexican ICAI, pp. 155-168.

Majumder, N., Poria, S., Gelbukh, A. y Cambria, E. (2017): «Deep learning-based document modeling for personality detection from text», IEEE Intelligent Systems 32, pp. 74-79.

Mendoza, M., Poblete, B. y Castillo, C. (2010): «Twitter under crisis: Can we trust what we RT?», en Proceedings of the 1st Workshop on Social Media Analytics, Nueva York, pp. 71-79.

Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X. y Cherry, C. (2016): «Semeval-2016 task 6: Detecting stance in tweets», en Proceedings of SemEval ’16, San Diego, pp. 31-41.

Nguyen D., Doğruöz, A. S., Rosé C. P. y de Jong, F. (2016): «Computational Sociolinguistics: A Survey», Computational Linguistics 42, pp. 537-593.

Schler, J., Koppel, M., Argamon, S. y Pennebaker, J. W. (2006): «Effects of age and gender on blogging», en Proceedings of the AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, Stanford, pp. 199-205.

Taulé, M., Martí, M.A., Rangel F., Rosso M., Bosco C. y Patti, V. (2017): «Overview of the task on stance and gender detection in tweets on Catalan independence at IberEval 2017», en Notebook Papers of the 2nd SEPLN IBEREVAL Workshop, Murcia, pp. 157-177.

Varol, O., Ferrara, E., Menczer, F. y Flammini, A. (2017): «Early detection of promoted campaigns on social media», en EPJ Data Science, 6.

Verbeke, W., Martens, D. y Baesens, B. (2014): «Social network analysis for customer churn prediction», en Applied Soft Computing 14, pp. 431-446.

Wang, W., Chen, L., Thirunarayan, K. y Sheth, A. P. (2012a): «Harnessing Twitter ‘BigData’ for Automatic Emotion Identification», en IEEE International Conference on Social Computing, Amsterdam, pp. 587-592.

Wang, X., Gerber, M. S. y Brown, D. E. (2012b): «Automatic crime prediction using events extracted from Twitter posts», en Proceedings of the International conference on social computing, behavioral-cultural modeling, and prediction, College Park, pp. 231-238.

Wijeratne, S., Balasuriya, L., Sheth, A. y Doran, D. (2017): «EmojiNet: An open service and API for Emoji Sense Discovery», en Proceedings of the ICWSM-2017.

Xu, J.-M., Jun, K.-S., Zhu, X. y Bellmore, A. (2012): «Learning from Bullying Traces in Social Media», en Proceedings of the 2012 Conference of the North American Chapter of the ACL: Human Language Technologies, Montreal, pp. 656-666.

Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K. y Tolmie, P. (2015): «Towards detecting rumours in social media», en Proceedings of the AAAI Workshop on AI for Cities, pp. 35-41.

Published

2021-12-18

How to Cite

Natural Language Processing Applied to Big Data in Social Networks: https://doi.org/10.31810/rsel.51.2.7. (2021). Revista Española De Lingüística, 51(2), 111-124. Retrieved from http://revista.sel.edu.es/index.php/revista/article/view/2067

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