Narrow Results

The cultivation of creativity is closely related to language learning. How to design the creativity promotion mechanism of English teaching in the public environment is the challenge faced by English teachers. With the advent of the era of big data, English teachers can apply the latest research results to classroom teaching. For example, the rational use of social media helps students to learn and communicate in the language, cultivate students’ creativity in learning English, and improves the quality of teaching. Corporate social media has become the most important way for corporate employees to record their lives, express opinions, share and communicate, and it is also one of the reliable and real-time sources of big data that reflects the true state of English learners. Real, accurate, and timely enterprise social media big data samples contain an enormous educational value, providing more possibilities for educational research. From the perspective of value, through sentiment analysis, topic mining, social network analysis, etc. on social media big data, learner portraits can be realized, thereby providing decision-making reference and support for stakeholders. This paper first builds a learning interest classification model based on TCNN-GRU deep learning, collects experimental data sets from an online English learner’s social media platform and performs learning interest classification and labeling, and then uses the TCNN-GRU model to determine the user’s learning interest tendency. On this basis, the concept of learning interest index is further proposed, and a neural network-based corporate social platform English learner portrait model is established. The experimental results show that, compared with the traditional machine learning model, convolutional neural network model, and recurrent neural network model, the TCNN-GRU model can obtain better results in learning interest classification.

Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.