الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Multilingual language models have decreased the barrier between
languages, as it will help overcome many problems, such as sentiment
analysis because the importance of this task is to make good decisions and
customize products. Obtaining information from one language can help other
languages generalize and understand a task more effectively. In this thesis,
we propose a general method for sentiment analysis of data that includes data
from many languages, which enables all applications to use sentiment
analysis results in a language-blind or language-independent manner. We
performed experiments on two language combinations (English and Arabic)
for sentence-level sentiment classification and found that the model with the
final setup after adding translations from one language to another and finetuning the multilingual language model for Twitter, was the best setup,
achieving . and . f -score for English and Arabic, respectively.
Our research focused on sarcasm detection, where we fine-tuned a
multilingual model. The exciting outcome is a single model that performs
well in both English and Arabic, showcasing effective cross-language
capabilities for sarcasm identification.