Sentiment Classification Method for Identification of Influential Learners in Social Networks Communities

The growth of social networking has gained much interest from the research community in recent years. Social networking technology as an e-learning tool seems promising for education instructors to combine distance education. Several analysis researches of social media were conducted for detection opinion leaders. While most of the existing algorithms proposed for communities determination are destined to commercial use, in this work, we present a new approach for detecting opinion leaders based on analyzing online learning community interactions. In fact, we aim to identify learners behaviors and attitudes in social network sites as productive online tools for learning. To achieve this purpose, we describe a method of performing detecting opinion leaders by using machine learning techniques. We focus on the application of text mining and sentiment analysis. The output of this work prove that education-based social network is very e ective and improvement for online communications. Experiments show the e ciency of the introduced method which can be helpful and pro table for education instructors.