Abstract: Sentiment Analysis (SA) is the process of determining the opinion of a text written in a natural language to be positive, negative, or neutral towards any specific target such as individuals, events, topics, products, organizations, services, etc. SA has its challenges, and one of them is sarcasm. Sarcasm is a form of communication that is intended to mock or harass someone by using words with the opposite of their literal meaning. It is often used to express a negative message using positive words. However, Sarcasm detection is one of the most challenging tasks in Natural Language Processing (NLP) especially for the Arabic language which has a rich nature and very complex morphology. It has gained relevance recently, due to its importance in improving the performance of various NLP applications including SA. In this paper, we propose an approach for automatic sarcasm detection in the Arabic text of Twitter data by using the Support Vector Machine (SVM) classifier to classify sarcastic tweets based on different N-gram features and using several weighting schemes. The experimental results obtained are promising. The best results by SVM classifier for all feature sets and several weighting schemes achieved overall accuracy equal to 86.60%, which these results are quite high especially regarding Arabic text.
Keywords: Automatic sarcasm detection; Sarcasm; Sentiment analysis; Text Mining; Support Vector Machine (SVM); Arabic language
| DOI: 10.17148/IJARCCE.2021.10801