DC Field | Value | Language |
dc.contributor.author | ALAHOUM, NAdjia | - |
dc.contributor.author | KOUADRI, AIchouch RAnia | - |
dc.date.accessioned | 2022-04-20T13:31:00Z | - |
dc.date.available | 2022-04-20T13:31:00Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/257 | - |
dc.description | Pr. Sidi Mohamed BENSLIMANE Encadreur Dr. Badia KLOUCHE Co-Encadreur | en_US |
dc.description.abstract | Sentiment analysis, also known as Opinion Mining, is a subfield of natural language
processing.
Since humans are omnipresent in today’s ultra-connected world thanks to social
networks, they are constantly expressing their opinions on multiple topics in a multitude
of occasions. Sentiment analysis is therefore crucial to identify the user’s opinion and
interests. When the user is native of the Arabic world, things are not different, on the
contrary, the richness and diversity of Arabic dialects makes social networks full of
opinions. It is therefore wise to try to exploit this mass of information to extract useful
information.
In this work, we focus on the sentiment analysis of Arabic dialects (focal point on
the Algerian dialect), in order to make a state of the art inventory of the different
approaches and techniques used | en_US |
dc.language.iso | fr | en_US |
dc.subject | Sentiment Analysis | en_US |
dc.subject | Opinion Mining | en_US |
dc.subject | NLP | en_US |
dc.subject | Arabic Dialect | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Classification Models | en_US |
dc.title | Analyse des sentiments Appliquée aux dialectes arabes | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master
|