Automation of understanding textual contents in social networks

Abstract

Today, the number of users of social network increases and a lot of users share opinions on different aspects of life every day. So the rate of colloquial written text increases dramatically as a medium of expressing ideas especially across the WWW. Therefore, social networks are rich sources of data for opinion mining and sentiment analysis. Arab colloquial dialects are languages that people used to communicate with each other in social networks. Recently, there is a massive amount of Arab colloquial data on Social networks. By increasing the available data, the needing for processing this data and using it is increased. However, most available tools and resources (morphological analyzers, disambiguation systems, annotated data, and parallel corpora) are for Modern Standard Arabic (MSA). Therefore, the need for the automatic transformation from Arab colloquial dialects to Modern Standard Arabic becomes urgent to use Modern Standard Arabic tools and resources for Arab colloquial dialects. The most famous colloquial is Egyptian colloquial dialect, which is considered the most widely used and understood dialect throughout the Arab world. Consequently, the focus of the proposed system is the Egyptian colloquial dialect to prove our approach.