Research on the Cultivation of Practical English Talents Based on a Big Data-Driven Model and Sentiment Dictionary Analysis

Amidst the ongoing wave of economic globalization, the societal demand for English proficiency is escalating, particularly for individuals adept in practical applications of the language. Recognizing the pivotal role of English reading as a cornerstone in language acquisition, there arises a need for personalized approaches tailored to individual interests, thereby necessitating an in-depth analysis of text emotions. Addressing the challenges in text classification within English reading courses, this study presents a novel method for text emotion analysis. Integrating sentiment dictionaries with BI-GRU networks, the proposed approach significantly enhances the efficiency of text emotion recognition while simultaneously fostering students' engagement. By segmenting the emotion dictionary based on polarity and extracting pertinent features, the study amalgamates these with BI-GRU features at the feature level. This fusion facilitates emotion classification within reading texts through sophisticated activation functions. Notably, the precision of recognizing positive, negative, and neutral emotions reaches an impressive 92.5%, marking a notable improvement over methods devoid of dictionary feature integration. This framework offers novel insights for future English reading material development and intelligent learning strategies to bolster student enthusiasm and chart a promising trajectory for cultivating practical English talents.