Abstract
This paper presents an approach towards bi-lingual sentiment analysis of tweets. Social networks being most advanced and popular communication medium can help in designing better government and business strategies. There are a number of studies reported that use data from social networks; however, most of them are based on English language. In this research, we have focused on sentiment analysis of bilingual dataset (English and Roman-Urdu) on topic of national interest (General Elections). Our experiments produced encouraging results with 76% of tweet’s sentiment strength classified correctly. We have also created a bi-lingual lexicon that stores the sentiment strength of English and Roman Urdu terms. Our lexicon is available at: https://sites.google. com/a/mcs.edu.pk/codteem/biling_senti
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
Preview
Unable to display preview. Download preview PDF.
References
Measuring tweets, http://blog.twitter.com/2010/02/measuring-tweets.html
Wu, Y., Wong, J., Deng, Y., Chang, K.: An Exploration of Social Media in Public Opinion Convergence: Elaboration Likelihood and Semantic Networks on Political Events. In: Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing (2011)
Bobicev, V., Maxim, V., Prodan, T., Burciu, N., Angheluş, V.: Emotions in Words: Developing a Multilingual WordNet-Affect. In: Gelbukh, A. (ed.) CICLing 2010. LNCS, vol. 6008, pp. 375–384. Springer, Heidelberg (2010)
Javed, I., Afzal, H.: Creation of bi-lingual Social Network Dataset using classifiers. Accepted in 10th International Conference on Machine Learning and Data Mining MLDM 2014, St. Petersburg, Russia, July 21-24 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Javed, I., Afzal, H., Majeed, A., Khan, B. (2014). Towards Creation of Linguistic Resources for Bilingual Sentiment Analysis of Twitter Data. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-07983-7_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07982-0
Online ISBN: 978-3-319-07983-7
eBook Packages: Computer ScienceComputer Science (R0)