|
| Titre : |
Machine and Deep Learning for Detection of Hate Speech in Videos |
| Type de document : |
texte imprimé |
| Auteurs : |
Kram,Amira, Auteur ; Toumi,Lyazid, Directeur de thèse |
| Editeur : |
Setif:UFA |
| Année de publication : |
2021 |
| Importance : |
1 vol (67 f .) |
| Format : |
29 cm |
| Langues : |
Français (fre) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Hate speech
O ensive language
Videos
Natural language
processing |
| Index. décimale : |
003.54 Théorie de l'information |
| Résumé : |
In the last decade, the social network became popular, and the emergence
of the phenomenon of hate speech has been observed. A streaming platforms
like YouTube contains a lot of videos in di erent languages. The Arabic
videos with hate speech become a noticeable problem that requires the development
of automated tools to detect o ensive language that a ects all
categories of people who use YouTube.
The Arabic language is a Semitic language, but unfortunately, there is a
few scienti c research concerning this language. The limited availability of
tools using the Arabic language makes to propose an automation tool is more
dicult. To our knowledge, this work is the rst that propose an automation
tool for detecting hate speech in Arabic videos.
In this master thesis, we propose to build an Arabic videos dataset from
the YouTube stream platform, how we annotated it, and using NLP techniques
for pre-processing step. Then, we applied popular machine learning
classi ers using BOW, ngrams, TF-IDF and we propose deep learning methods
to solve our problem. Finally, the experiments on the used dataset show
that the support vector machines model gives the best performance for our
problem than the best known other classi ers.
|
| Côte titre : |
MAI/0475 |
| En ligne : |
https://drive.google.com/file/d/1YUryxThPYFAP9g3mN0eCTHG4ZpUP8VT_/view?usp=shari [...] |
| Format de la ressource électronique : |
pdf |
Machine and Deep Learning for Detection of Hate Speech in Videos [texte imprimé] / Kram,Amira, Auteur ; Toumi,Lyazid, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (67 f .) ; 29 cm. Langues : Français ( fre)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Hate speech
O ensive language
Videos
Natural language
processing |
| Index. décimale : |
003.54 Théorie de l'information |
| Résumé : |
In the last decade, the social network became popular, and the emergence
of the phenomenon of hate speech has been observed. A streaming platforms
like YouTube contains a lot of videos in di erent languages. The Arabic
videos with hate speech become a noticeable problem that requires the development
of automated tools to detect o ensive language that a ects all
categories of people who use YouTube.
The Arabic language is a Semitic language, but unfortunately, there is a
few scienti c research concerning this language. The limited availability of
tools using the Arabic language makes to propose an automation tool is more
dicult. To our knowledge, this work is the rst that propose an automation
tool for detecting hate speech in Arabic videos.
In this master thesis, we propose to build an Arabic videos dataset from
the YouTube stream platform, how we annotated it, and using NLP techniques
for pre-processing step. Then, we applied popular machine learning
classi ers using BOW, ngrams, TF-IDF and we propose deep learning methods
to solve our problem. Finally, the experiments on the used dataset show
that the support vector machines model gives the best performance for our
problem than the best known other classi ers.
|
| Côte titre : |
MAI/0475 |
| En ligne : |
https://drive.google.com/file/d/1YUryxThPYFAP9g3mN0eCTHG4ZpUP8VT_/view?usp=shari [...] |
| Format de la ressource électronique : |
pdf |
|
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