Article: Sentiment feedback for English writing using BERT-LSTM hybrid modelling Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.47 pp.20 - 35 Abstract: Addressing the challenges of subjectivity and delayed feedback in English writing sentiment analysis, particularly for non-native academic contexts, this study proposes an automated framework based on a hybrid bidirectional encoder representations from transformers-long short-term memory model. The model integrates Bert's contextual encoding capabilities with long short-term memory's sequential modelling strengths, leveraging attention mechanisms for three-dimensional granular sentiment analysis (tendency-intensity-object). Evaluated on a 2020-2023 subset of the international English language testing system writing dataset, the model achieves a sentiment classification accuracy of 91.5% (F1-score 0.89), outperforming baseline models by 12.3%. Educational applicability testing showed an 87.2% teacher approval for reducing feedback workload, confirming its efficacy in supporting writing pedagogy decisions. Key innovations include a multi-task with domain adaptation and a visualised feedback system, establishing a new paradigm for intelligent educational tool with real-time intervention capabilities. Inderscience Publishers - linking academia, business and industry through research

Title: Sentiment feedback for English writing using BERT-LSTM hybrid modelling

Authors: Yizhou He

Addresses: School of Humanity, Xinyu University, Xinyu, 338004, China

Abstract: Addressing the challenges of subjectivity and delayed feedback in English writing sentiment analysis, particularly for non-native academic contexts, this study proposes an automated framework based on a hybrid bidirectional encoder representations from transformers-long short-term memory model. The model integrates Bert's contextual encoding capabilities with long short-term memory's sequential modelling strengths, leveraging attention mechanisms for three-dimensional granular sentiment analysis (tendency-intensity-object). Evaluated on a 2020-2023 subset of the international English language testing system writing dataset, the model achieves a sentiment classification accuracy of 91.5% (F1-score 0.89), outperforming baseline models by 12.3%. Educational applicability testing showed an 87.2% teacher approval for reducing feedback workload, confirming its efficacy in supporting writing pedagogy decisions. Key innovations include a multi-task with domain adaptation and a visualised feedback system, establishing a new paradigm for intelligent educational tool with real-time intervention capabilities.

Keywords: sentiment analysis; English writing assessment; BERT-LSTM hybrid model; attention mechanism; educational feedback system.

DOI: 10.1504/IJICT.2025.150609

International Journal of Information and Communication Technology, 2025 Vol.26 No.47, pp.20 - 35

Received: 18 Aug 2025
Accepted: 30 Oct 2025
Published online: 17 Dec 2025 *

Title: Sentiment feedback for English writing using BERT-LSTM hybrid modelling

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