Walaa Medhat

Walaa Medhat, Ahmed H. Yousef, Hoda Korashy

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 removingED 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.

Walaa Medhat, Ahmed H. Yousef, Hoda Korashy

This paper proposes a methodology to prepare corpora in Arabic language from online social network (OSN) and review site for Sentiment Analysis (SA) task. The paper also proposes a methodology for generating a stopword list from the prepared corpora. The aim of the paper is to investigate the effect of removing stopwords on the SA task. The problem is that 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 and a corpus-based list to be used with the OSN corpora. We compare the efficiency of text classification when using the generated lists 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, unigrams and bigram. The experiments show that the general lists containing the Egyptian dialects words give better performance than using lists of MSA stopwords only.