TITLE:
Framework to Classify and Analyze Social Media Content
AUTHORS:
Hayfa Aleid, Amjad Abdulrahman Alkhalaf, Aseel Helal Taemees, Hanan Saeed Almurished, Khawla Abdullah Alharbi, Nawal Hussein Alanazi
KEYWORDS:
Framework, Classification, Social Media Network, Support Vector Machine, Machine Learning, My Interest
JOURNAL NAME:
Social Networking,
Vol.7 No.2,
April
12,
2018
ABSTRACT:
In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.