Multimedia sentiment analysis using modified CNN and rnn models / Youssef Saad Ghatas ; Supervised Elsayed Hemayed

By:

Youssef Saad Ghatas

Contributor(s):

Elsayed Hemayed []

Material type: Text

TextLanguage: English Publication details: Cairo : Youssef Saad Ghatas , 2017Description: 70 P. : charts , facsimiles ; 30cmOther title:

دراسة لتصنيف الآراء ذات الوسائط المتعددة عن طريق نموذج يتضمن شبكة عصبية التفافية و شبكة عصبية متكررة [Added title page title]

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Issued also as CD

Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: Multimedia Sentiment Analysis is considered a great challenge, given the diversity of data it combines and the informality challenges it produces. Firstly, this study divides the problem into textual and visual fields and study the effect of using different CNN and RNN models. Secondly, the proposed model is compared to the top candidate and the state-of-the-art model. The proposed model -which uses deep CNN model for images and RCNN model for texts- outperforms the other tested models and the state-of-the-art model on the same dataset in both accuracy and F1 score with absolute improvement of about 5% and relative error improvement of more than 25%. Finally, a sensitivity test is conducted to test the effect of different values for some important parameters of the proposed model

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Holdings
Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.06.M.Sc.2017.Yo.M (Browse shelf(Opens below))		Not for loan	01010110074801000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.06.M.Sc.2017.Yo.M (Browse shelf(Opens below))	74801.CD	Not for loan	01020110074801000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Multimedia Sentiment Analysis is considered a great challenge, given the diversity of data it combines and the informality challenges it produces. Firstly, this study divides the problem into textual and visual fields and study the effect of using different CNN and RNN models. Secondly, the proposed model is compared to the top candidate and the state-of-the-art model. The proposed model -which uses deep CNN model for images and RCNN model for texts- outperforms the other tested models and the state-of-the-art model on the same dataset in both accuracy and F1 score with absolute improvement of about 5% and relative error improvement of more than 25%. Finally, a sensitivity test is conducted to test the effect of different values for some important parameters of the proposed model

Issued also as CD

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