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Emotion Detection in Arabic Texts Extracted from Twitter Network by Using Machine Learning Techniques

المصدر: مجلة المعهد العالي للدراسات النوعية
الناشر: المعهد العالي للدراسات النوعية
المؤلف الرئيسي: Alnahhas, Ammar (Author)
مؤلفين آخرين: Alkhateeb, Hiba Mohamed (Co-Author)
المجلد/العدد: مج3, ع6
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2023
الشهر: يوليو
الصفحات: 483 - 497
ISSN: 2786-0256
رقم MD: 1416719
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EduSearch
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041 |a eng 
044 |b مصر 
100 |9 749415  |a Alnahhas, Ammar   |e Author 
245 |a Emotion Detection in Arabic Texts Extracted from Twitter Network by Using Machine Learning Techniques 
260 |b المعهد العالي للدراسات النوعية  |c 2023  |g يوليو 
300 |a 483 - 497 
336 |a بحوث ومقالات  |b Article 
520 |b Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. Just a few studies utilized sentiment analysis of social media using a machine learning approach. These studies focused more on sentiment analysis of Twitter tweets in the English language and did not pay more attention to other languages such as Arabic. The algorithms applied in the previous studies were included: Support Vector Machine Model, Neural Network Model, Stochastic Gradient Descent model 4-K nearest neighbors model, Naive Bayes model, Logistic Regression model, Ensemble learning Stacking Method (Random forest, Neural Network and KNN), Ensemble learning Extreme Gradient Boosting Machine (XGBoost).While the decision tree. (DT) model was used as a baseline classification comparison with previous best performing models This study proposes a machine learning model to analyze the Arabic tweets from Twitter. In this model, we apply Word2Vec for word embedding and Several algorithms have been applied in order to reach the highest accuracy in analyzing the sentiments of Twitter users, The best performing model was obtained in our data set. By getting the best hyperparameters This is when we use hyperparameter optimization, where we use Grid Search technology The results show that applying word embedding with an ensemble XGBoost achieved good improvement on average of F1 score and the best of accuracy. 
653 |a التعليم الآلي  |a النصوص العربية  |a وسائل التواصل الاجتماعي  |a التعليم الجماعي 
700 |9 749416  |a Alkhateeb, Hiba Mohamed  |e Co-Author 
773 |4 التعليم الخاص  |6 Education, Special  |c 012  |e Journal of the Higher Institute for Specific Studies  |f Mağallaẗ Al-Maʿhad Al-ʿālī Lil Dirāsāt Al-Nawʿiyyaẗ  |l 006  |m مج3, ع6  |o 2399  |s مجلة المعهد العالي للدراسات النوعية  |v 003  |x 2786-0256 
856 |u 2399-003-006-012.pdf 
930 |d y  |p y  |q n 
995 |a EduSearch 
999 |c 1416719  |d 1416719 

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