Titre :	Sentiment analysis using machine learning
Type de document :	texte imprimé
Auteurs :	Zakaria Rachedi, Auteur ; Mohamed Ali Badreddine ; mohamed Chafia Kara, Directeur de thèse
Editeur :	Sétif:UFS
Année de publication :	2023
Importance :	1 vol (54 f.)
Format :	29 cm
Langues :	Anglais (eng)
Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Informatique
Index. décimale :	004 - Informatique
Résumé :	In our real world there are a lot of languages that are used for communication nowadays and for each language there are also many dialects. An important subfield of Artificial Intelligence is Machine learning ML. Natural Language Processing is the part that deals with natural languages automatically. The most of works and researches that are being conducted generally are implicated with English, but not so much with Arabic language. In our work, we use different machine learning algorithms to classify, based on the sentiment expressed, comments from different Arabic dialects which we have taken from social media (mainly Facebook and twitter). In our work we have built classification models and evaluated them with metrics (accuracy, F1score, recall, precision, ROC curve). Then we tested the P a g e 3 | 54 performance of the models with one dialect and wth the mixed dialects.
Côte titre :	MAI/0811
En ligne :	https://drive.google.com/file/d/1GIyyNPk_I2TfeIS31RK0eeTFaDQvRmub/view?usp=drive [...]
Format de la ressource électronique :	pdf

Sentiment analysis using machine learning [texte imprimé] / Zakaria Rachedi, Auteur ; Mohamed Ali Badreddine ; mohamed Chafia Kara, Directeur de thèse . - [S.l.] : Sétif:UFS, 2023 . - 1 vol (54 f.) ; 29 cm.
Langues : Anglais (eng)

Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Informatique
Index. décimale :	004 - Informatique
Résumé :	In our real world there are a lot of languages that are used for communication nowadays and for each language there are also many dialects. An important subfield of Artificial Intelligence is Machine learning ML. Natural Language Processing is the part that deals with natural languages automatically. The most of works and researches that are being conducted generally are implicated with English, but not so much with Arabic language. In our work, we use different machine learning algorithms to classify, based on the sentiment expressed, comments from different Arabic dialects which we have taken from social media (mainly Facebook and twitter). In our work we have built classification models and evaluated them with metrics (accuracy, F1score, recall, precision, ROC curve). Then we tested the P a g e 3 | 54 performance of the models with one dialect and wth the mixed dialects.
Côte titre :	MAI/0811
En ligne :	https://drive.google.com/file/d/1GIyyNPk_I2TfeIS31RK0eeTFaDQvRmub/view?usp=drive [...]
Format de la ressource électronique :	pdf