Synonyms
Glossary
- NLP :
-
Natural Language Processing
Definition
The term “microtext” was proposed by US Navy researchers (Dela Rosa and Ellen 2009) to describe a type of written text document that has three characteristics: (a) it is very short, typically one or two sentences, and possibly as little as a single word; (b) it is written in an informal manner and unedited for quality and thus may use loose grammar, a conversational tone, vocabulary errors, and uncommon abbreviations and acronyms; and (c) it is semi-structured in the NLP sense, in that it includes some metadata such as a time stamp, an author, or the name of a field it was entered into. Microtexts have become omnipresent in today’s world: they are notably found in online chat discussions; online forum posts; user comments posted on online material such as videos, pictures, and news stories; Facebook newsfeeds and Twitter updates; Internet...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldwin T, Chai JY (2011) Beyond normalization: pragmatics of word form in text messages. In: 5th international joint conference on natural language processing, Chiang Mai, 8–13 Nov 2011
Barbosa L, Feng, J (2010) Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational Linguistics, Beijing, pp 36–44
Baron N, Ling R (2007) Text messaging and IM: linguistic comparison of American college data. J Lang Psychol Stud 26:291–298
Chen L, Wang W, Sheth AP (2012) Are Twitter users equal in predicting elections? A study of user groups in predicting 2012 U.S. republican presidential primaries. In: SocInfo 2012, Lausanne. LNCS 7710, Springer, pp 379–392
Cormack GV, Gómez Hidalgo JM, Puertas Sánz E (2007) Spam filtering for short messages. In: Proceedings of the 16th ACM conference on information and knowledge management (ACM CIKM’07), Lisbon, pp 313–320
Cvijikj IP, Michahelles F (2011) Monitoring trends on Facebook. In: Ninth IEEE international conference on dependable, autonomic and secure computing, Zurich, 12–14 Dec 2011, pp 895–202
Dela Rosa K, Ellen J (2009) Text classification methodologies applied to micro-text in military chat. In: Proceedings of the international conference on machine learning and applications, Miami Beach, pp 710–714
Dong H, Hui SC, He Y (2006) Structural analysis of chat messages for topic detection. Online Inf Rev 30(5):496–516
Ellen J (2011) All about microtext: a working definition and a survey of current microtext research within artificial intelligence and natural language processing. In: ICAART (1), Rome, pp 329–336
Ferrara K, Brunner H, Whittemore G (1991) Interactive written discourse as an emergent register. Writ Commun 8:8–34
Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Technical report, Stanford
Healy M, Delany S, Zamolotskikh A (2005) An assessment of case-based reasoning for short text messages. In: Creaney N (ed) Proceedings of the 16th Irish conference on artificial intelligence and cognitive science, pp 257–266
Kolenda T, Hansen LK, Larsen J (2001) Signal detection using ICA: application to chat room topic spotting. In: Proceedings of the third international conference on independent component analysis and blind source separation, San Diego, pp 540–545
Liu F, Weng F, Wang B, Liu Y (2011) Insertion, deletion, or substitution?: normalizing text messages without pre-categorization nor supervision. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Portland, vol 2, pp 71–76
Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, Valletta. European Language
Paolillo JC (1999) The virtual speech community: social network and language variation on IRC. In: Proceedings of the 32nd annual Hawaii international conference on system sciences, Maui
Petrovic S, Osborne M, Lavrenko V (2010) The Edinburgh Twitter corpus. In: Proceedings of the NAACL HLT workshop on computational linguistics in a world of social media, Los Angeles pp 25–26
Ritterman J, Osborne M, Klein E (2009) Using prediction markets and Twitter to predict a swine flu pandemic. In: 1st international workshop on mining social media – 13th conference of the Spanish association for artificial intelligence
Takahashi T, Tomioka R, Yamanishi K (2011) Discovering emerging topics in social streams via link anomaly detection. In: 11th IEEE international conference on data mining, Tokyo, 11–14 Dec 2011, pp 1230–1235
Wang AH (2010) Don’t follow me – spam detection in Twitter. In: Proceedings of the international conference on security and cryptography (SECRYPT 2010), Athens, pp 142–151
Wu T, Khan FM, Fisher TA, Shuler LA, Pottenger WM (2002) Posting act tagging using transformation-based learning. In: The proceedings of the workshop on foundations of data mining and discovery, IEEE international conference on data mining (ICDM’02), Dec 2002
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Khoury, R., Khoury, R., Hamou-Lhadj, A. (2014). Microtext Processing. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_353
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6170-8_353
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6169-2
Online ISBN: 978-1-4614-6170-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering