arXiv subjects

Walaa Medhat

Publications and source records attributed to Walaa Medhat.

3 recordsLinked to original sources

arXiv2015

Egyptian Dialect Stopword List Generation from Social Network Data

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.

cs.CL↗

arXiv2014

Combined Algorithm for Data Mining using Association rules

Association Rule mining is one of the most important fields in data mining and knowledge discovery. This paper proposes an algorithm that combines the simple association rules derived from basic Apriori Algorithm with the multiple minimum support using maximum constraints. The algorithm is implemented, and is compared to its predecessor algorithms using a novel proposed comparison algorithm. Results of applying the proposed algorithm show faster performance than other algorithms without scarifying the accuracy.

cs.DB↗

arXiv2014

Corpora Preparation and Stopword List Generation for Arabic data in Social Network

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.

cs.CL↗