Abstract
The SNS became popularized by computer, mobile devices, and tablets that are accessible to the Internet. Among SNS, Twitter posts the words of short texts and, it shares information. Twitter texts are the optimal data to extract new information, but as it may contain the information within the limited number of words, there are various limitations. To improve accuracy of extracting information within Twitter texts, the process of calibrating misspelled words shall be taken in advance. In conventional studies to correct the misspelled words of Twitter texts, the relationship between misspelled words and correcting words was resolved by concerning the dependency of co-occurrence words with misspelled words within sentences and morphophonemic similarity, but since the frequency of co-occurrence words of misspelled words is not concerned, it has not resolved to correct misspelled words completely. In this paper, to correct misspelled words in Twitter texts, the use of the character n-gram method concerning spelling information and the word n-gram method concerning frequency of co-occurrence words are to be proposed.
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
Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P.N., Zhao, B.Y.: User intereactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Compter Systems, pp. 205–218 (2009)
Kim, J., Ko, B., Jeong, H., Kim, P.: A method for extracting topics in news twitter. Int. J. Softw. Eng. Appl. 7(2), 1–6 (2013)
Vespignani, A.: Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8, 32–39 (2012)
Beaufort, R., Roekhaut, S., Cougnon, L.A., Fairon, C.: A hybrid rule/model-based finite-state framework for normalizing SMS messages. In: Proceedings of the 48th Annual Meeting of the ACL (ACL 2010), pp. 770–779 (2010)
Choudhury, M., Saraf, R., Jain, V., Mukherjee, A., Sarkar, S., Basu, A.: Investigation and modeling of the structure of texting language. Int. J. Doc. Anal. Recogn. 10(3), 157–174 (2007)
Hassan, H., Menezes, A.: Social text normalization using contextual graph random walks. In: The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 1577–1586 (2013)
Kobus, C., Yvon, F., Damnati, G.: Normalizing SMS: are two metaphors better than one? In: The 22nd International Conference on Computational Linguistics (COLING 2008), pp. 441–448 (2008)
Chen, Y.: Improving text normalization using character-blocks based models and system combination. In: The 24th International Conference on Computational Linguistics (COLING 2012), pp. 1587–1602 (2012)
Jung, J.J.: Online named entity recognition method for microtexts in social networking services: a case study of twitter. Expert Syst. Appl. 39(9), 8066–8070 (2012)
Longest common subsequence problem. http://en.wikipedia.org/wiki/Longest_common_subsequence_problem
Acknowledgments
This research was supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea (NRF-2014H1C1A1073115) and This research was supported by SW Master’s course of hiring contract Program grant funded by the Ministry of Science, ICT and Future Planning (H0116-16-1013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Kim, J., Lee, E., Hong, T., Kim, P. (2017). Correcting Misspelled Words in Twitter Text. In: Jung, J., Kim, P. (eds) Big Data Technologies and Applications. BDTA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-58967-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-58967-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58966-4
Online ISBN: 978-3-319-58967-1
eBook Packages: Computer ScienceComputer Science (R0)