Abstract

Recently, there are wide numbers of users that use the social network like Twitter, Facebook, MySpace to share various kinds of resources, express their opinions, thoughts, messages in real time. Thus, increase the amount of electronic content that generated by users. Sentiment analysis becomes a very interesting topic in research community. Thereby, we need to give more attention to Arabic sentiment analysis. This paper discusses the challenges and obstacles when analyze the sentiment analysis of informal Arabic, the social media. The most of recent research sentiment analysis conduct for English text. Also, when the research works in Arabic sentiment analysis, they focus in formal Arabic. However, most of social media network use the informal Arabic (colloquial) such as Twitter and YouTube website. This paper investigates the problems and the challenges to identify sentiment in informal Arabic language which is mostly used when users express their opinions and feelings in context of twitter and YouTube Arabic content.

Highlights

  • Arabic is a Semitic language spoken by more than 330 million people as a native language

  • Arabic natural language processing (NLP) applications must deal with the complex nature of the Arabic language

  • Arabic is written from right to left and no capitalization is used for nouns, which is a necessary feature in text mining

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Summary

Introduction

Arabic is a Semitic language spoken by more than 330 million people as a native language. Arabic is a highly structured and derivational language, in which morphology has a very important role. Arabic natural language processing (NLP) applications must deal with the complex nature of the Arabic language. Arabic is written from right to left and no capitalization is used for nouns, which is a necessary feature in text mining. The Arabic language contains 28 letters and, in addition, the Hamza (‫)ء‬. In Arabic, letters change their shape according to their position in the word (beginning, middle, or end) [1].

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