Application in Social Network English Learning Based on Virtual Cloud Technology Combined with Essential Articles Classification

Abstract

When searching for articles relating to English grammar, general search engines only compare keywords but do not analyze whether the articles are related to learning. This study proposed an English grammar learning articles classification system that could analyze the collected articles. The proposed system used TF-IDF to summarize the important information of the articles and the neural text categorizer neural network architecture for training the classification and summarization of articles relating to learning English. As the computational cost of such work was high, a private cloud structure was built using multiple virtual machines to enhance the effectiveness in all respects and help learners find accurate English grammar learning articles. The concept of social networking was combined with the English platform adaptive test mechanism developed by this study, which allowed the learners to search for appropriate English learning articles. The learners were asked to provide their opinions and feedback on the found articles, which were ultimately sent back to the proposed cloud text classification system to determine the most suitable articles for improving the English level of the learners.