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dc.contributor.advisorAbd Alhaq , Hamed ,Othman Othman
dc.contributor.authorAsaad, Murad
dc.date.accessioned2018-01-29T09:29:56Z
dc.date.available2018-01-29T09:29:56Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/20.500.11888/13089
dc.description.abstractSentiment Analysis is the process of determining the sentiment of a text written in natural language. A number of lexicons are available for English language. In this paper, we present a sentiment lexicon for Arabic language written Palestinian dialect. In addition, we present the process of collecting and annotating data from Facebook social media Palestinian public pages. Moreover, we present results after using machine-learning algorithms to enhance the classification prediction of Positive and Negative classes. The experiments were conducted over testing data taken from the data used to build the lexicon and we were able to achieve over 90% F-score and about 80% F-score for unseen data. is the process of determining the sentiment of a text written in natural language. A number of lexicons are available for English language. In this project, we present a sentiment lexicon for Arabic language written Palestinian dialect with real time classification using Apache spark and Elasticsearch. In addition, we present the process of collecting and annotating data from Facebook social media Palestinian public pages. Moreover, we present results after using machine-learning algorithms to enhance the classification prediction of Positive and Negative classes. The experiments were conducted over testing data taken from the data used to build the lexicon and we were able to achieve over 90% F-score and about 80% F-score for unseen data.en_US
dc.language.isoen_USen_US
dc.subjectSentiment Analysis, Sentiment Lexicon, Data Analysis, Text classification, machine learning, Spark, Elasticsearchen_US
dc.titleSentiment Analysis for Palestinian Dialect Arabic Languageen_US
dc.typeGraduation Projecten_US


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