Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a large number of texts and their diversity in all areas of life. In this manuscript, we apply four algorithms to classify tweets written in the Algerian dialect. To extract feelings, we used six features based on three polarities. In the presented work, we manually annotate a corpus of 2,891 texts and create an Algerian lexicon of idioms that contains 1328 annotated words. Our results show that there are improvements gained in the accuracy of the system, where we have achieved a better accuracy of 85.31.

• sentiment analysis
• Algerian dialect
• Arabic language
• tweets
• lexicon construction
• idioms
• annotated corpus
• social media text

• Sentiment Analysis and Opinion Mining
• Hate Speech and Cyberbullying Detection
• Emotion and Mood Recognition
• sentiment analysis
• Algerian dialect
• Arabic language
• tweets
• lexicon construction
• idioms
• annotated corpus
• social media text
• Artificial Intelligence
• Experimental and Cognitive Psychology
• Computer Science
• Psychology