A survey of text mining in social media facebook and twitter perspectives

• Main Authors: Said A., Salloum, Mostafa, Al-Emran, Azza, Abdel Monem, Khaled, Shaalan
• Format: Article
• Language: English
• Published: 2017
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/17141/1/A%20survey%20of%20text%20mining%20in%20social%20media%20facebook%20and%20twitter%20perspectives.pdf; http://umpir.ump.edu.my/id/eprint/17141/; https://www.researchgate.net/profile/Said_Salloum/publication/313075362_A_Survey_of_Text_Mining_in_Social_Media_Facebook_and_Twitter_Perspectives/links/588f82f592851c9794c471cf/A-Survey-of-Text-Mining-in-Social-Media-Facebook-and-Twitter-Perspectives.pdf
• Institution: Universiti Malaysia Pahang

Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR) and data mining. Natural Language Processing (NLP) techniques were used to extract knowledge from the textual text that is written by human beings. Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period. Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other. It becomes a common practice to not write a sentence with correct grammar and spelling. This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order. Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites. This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data. This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world. Results of this survey can serve as the baselines for future text mining research.