Social Media Data Mining in English Learners Oral Expression Intelligent Terminal System

This paper discusses the design and implementation of an intelligent terminal system for oral English learners based on social media data mining. Then, this paper proposes a spoken English recognition model combining social media data analysis and machine learning algorithms, aiming to provide a personalized spoken English practice and feedback mechanism for non-native English learners. The study first collected oral English data from social media platforms, and extracted effective language features through data cleaning and preprocessing. Then, using deep learning techniques, especially recurrent neural network (RNN) and long short-term memory network (LSTM), the paper constructs a spoken language recognition algorithm model. The model can accurately capture subtle differences in intonation, rhythm and pronunciation in spoken expression, providing learners with instant speech recognition and error correction. In order to verify the validity of the model, extensive simulation experiments are carried out in this paper. The experimental results show that compared with the traditional speech recognition system, the proposed model shows higher accuracy and better adaptability in recognizing spoken English learners. In addition, the system can also adjust the difficulty of exercises according to the progress trend of learners to achieve personalized teaching.