Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) collected from Facebook are used to evaluate the model. Experiments showed that the model obtained good results, as the accuracy of the model was 91.1, 92.4, and 92.5% for IADS, ACMID, and IAD, respectively. The results of the model outperformed previous works for all datasets.
Abstract
The Purpose of This Research is The Main Factors In out Comes Phenomena From Primary School Which in Creased in Lost Period in Iraq And to Find Solutions to The This Problem.
In Order to Achieve Al The Aim The Research Choose a Systematic Random Sample of School Records For Students in Some Primary Schools in Karkh and Rusafa and Year of Study (2010-2015) and Size (40) Samples, included (16) Variable , Collected in Form Prepared by The Research As a Way to Analyze The Data.
Remember to Summarize The (6) Main components Pay a Student to Drop out of Primary Schools in The Province of Baghdad are Arranged As Follows:
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