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An opinion mining extractor for arabic social media / Amr Magdy Mohamed Sayed ; Supervised Aly Aly Fahmy , Reem Bahgat

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Amr Magdy Mohamed Sayed , 2017Description: 96 Leaves : charts ; 30cmOther title:
  • مستخلص الآراء لمواقع التواصل الاجتماعي باللغة العربية [Added title page title]
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Dissertation note: Thesis (M.Sc. ) - Cairo University - Faculty of Computers and Information - Department of Computer Science Summary: Emotional analysis have recently become effective tools to discover peoples attitudes towards events. This paper aims to analyse people emotions from tweets. We propose a time emotional analysis framework that consists of four components namely annotating tweets, classifying at many levels, clustering on some aspects, and analysing aspects over specific time. Our contribution is two-fold. First, our framework effectively analyzes people emotional trends over time, at different fine-granularity levels (tweets, expressions, and aspects). Second, we developed a lightweight clustering algorithm that utilizes the short length of tweets
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Am.O (Browse shelf(Opens below)) Not for loan 01010110075109000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Am.O (Browse shelf(Opens below)) 75109.CD Not for loan 01020110075109000

Thesis (M.Sc. ) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Emotional analysis have recently become effective tools to discover peoples attitudes towards events. This paper aims to analyse people emotions from tweets. We propose a time emotional analysis framework that consists of four components namely annotating tweets, classifying at many levels, clustering on some aspects, and analysing aspects over specific time. Our contribution is two-fold. First, our framework effectively analyzes people emotional trends over time, at different fine-granularity levels (tweets, expressions, and aspects). Second, we developed a lightweight clustering algorithm that utilizes the short length of tweets

Issued also as CD

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