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The Egyptian Journal of Language Engineering
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Medhat, W., Yousef, A., Korashy, H. (2015). Egyptian Dialect Stopword List Generation from Social Network Data. The Egyptian Journal of Language Engineering, 2(1), 43-55. doi: 10.21608/ejle.2015.60258
Walaa Medhat; Ahmed Yousef; Hoda Korashy. "Egyptian Dialect Stopword List Generation from Social Network Data". The Egyptian Journal of Language Engineering, 2, 1, 2015, 43-55. doi: 10.21608/ejle.2015.60258
Medhat, W., Yousef, A., Korashy, H. (2015). 'Egyptian Dialect Stopword List Generation from Social Network Data', The Egyptian Journal of Language Engineering, 2(1), pp. 43-55. doi: 10.21608/ejle.2015.60258
Medhat, W., Yousef, A., Korashy, H. Egyptian Dialect Stopword List Generation from Social Network Data. The Egyptian Journal of Language Engineering, 2015; 2(1): 43-55. doi: 10.21608/ejle.2015.60258

Egyptian Dialect Stopword List Generation from Social Network Data

Article 4, Volume 2, Issue 1, April 2015, Page 43-55  XML PDF (1.21 MB)
Document Type: Original Article
DOI: 10.21608/ejle.2015.60258
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Authors
Walaa Medhat email 1; Ahmed Yousef2; Hoda Korashy2
1School of Electronic Engineering, Canadian International College, Cairo campus of CBU
2Computers & systems Department, Faculty of Engineering, Ain Shams University
Abstract
This paper proposes a methodology for generating a stopword list from online social network (OSN) corpora in Egyptian Dialect (ED). The aim of the paper is to investigate the effect of removing ED stopwords on the Sentiment Analysis (SA) task. The stopwords lists generated before were on Modern Standard Arabic (MSA) which is not the common language used in OSN. We have generated a stopword list of Egyptian dialect to be used with the OSN corpora. We compare the efficiency of text classification when using the generated list along with previously generated lists of MSA and combining the Egyptian dialect list with the MSA list. The text classification was performed using Naïve Bayes and Decision Tree classifiers and two feature selection approaches, unigram and bigram. The experiments show that removing ED stopwords give better performance than using lists of MSA stopwords only.
Keywords
Sentiment Analysis; Feature Selection; Removing stopwords; Arabic Dialect
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