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المؤلفBadaro, Gilbert
المؤلفBaly, Ramy
المؤلفAkel, Rana
المؤلفFayad, Linda
المؤلفKhairallah, Jeffrey
المؤلفHajj, Hazem
المؤلفEl-Hajj, Wassim
المؤلفShaban, Khaled Bashir
تاريخ الإتاحة2022-12-21T10:01:45Z
تاريخ النشر2015
اسم المنشور2nd Workshop on Arabic Natural Language Processing, ANLP 2015 - held at 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015 - Proceedings
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.18653/v1/W15-3203
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37485
الملخصMost advanced mobile applications require server-based and communication. This often causes additional energy consumption on the already energy-limited mobile devices. In this work, we provide to address these limitations on the mobile for Opinion Mining in Arabic. Instead of relying on compute-intensive NLP processing, the method uses an Arabic lexical resource stored on the device. Text is stemmed, and the words are then matched to our own developed ArSenL. ArSenL is the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) developed using a combination of English SentiWordnet (ESWN), Arabic WordNet, and the Arabic Morphological Analyzer (AraMorph). The scores from the matched stems are then processed through a classifier for determining the polarity. The method was tested on a published set of Arabic tweets, and an average accuracy of 67% was achieved. The developed mobile application is also made publicly available. The application takes as input a topic of interest and retrieves the latest Arabic tweets related to this topic. It then displays the tweets superimposed with colors representing sentiment labels as positive, negative or neutral. The application also provides visual summaries of searched topics and a history showing how the sentiments for a certain topic have been evolving. ACL 2015. All rights reserved.
راعي المشروعThis work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرAssociation for Computational Linguistics (ACL)
الموضوعEnergy utilization
Sentiment analysis
Energy
Energy-consumption
Large-scales
Lexical resources
Lexicon-based
Mobile applications
Opinion mining
Sentiment lexicons
Server communications
Server-based
Mobile computing
العنوانA light lexicon-based mobile application for sentiment mining of arabic tweets
النوعConference Paper
الصفحات18-25
dc.accessType Abstract Only


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