Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic
datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper,
a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process.
The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis
and sarcasm detection shared task. |
