AI-Driven Ethical Criticism of Literary Narrative: An Example from Chinese New Century Literature

The rapid development of Artificial Intelligence (AI) technology has profoundly affected the digitization process in the field of literature, and the application of AI technology in the ethical criticism of digital literary narratives has become more and more widespread.In this paper, combining RoBERTa pre-training model, BiLSTM, and attention mechanism, we constructed a sentiment analysis model based on RoBERTa-BiLSTM-Attention, and analyzed the sentiment trend of the novel text with the object of Chinese New Century Literature "The Riverbank" as a basis for the AI to generate literary narrative ethical criticism.The experimental results show that this paper's model with the multi-head attention mechanism layer improves 3.22% and 1.65% in the two evaluation indexes of accuracy and F1, respectively, compared with the RoBERTa-BiLSTM model without the addition of the multi-head attention mechanism layer, which proves the effectiveness of this paper's model in introducing the attention mechanism.Meanwhile, comparing with other models, the accuracy and F1 value of this paper's model are optimal, reaching 88.35% and 86.52%, respectively, which indicates that this paper's model is suitable for the task of recognizing the emotions of literary texts.

Publication Details

Journal
International journal for housing science and its applications.
Published
2025-08-15
DOI
https://doi.org/10.70517/ijhsa463306
Primary Topic
Medical Research and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Driven Ethical Criticism of Literary Narrative: An Example from Chinese New Century Literature

International journal for housing science and its applications.
Medical Research and Treatments
article

AI-Driven Ethical Criticism of Literary Narrative: An Example from Chinese New Century Literature

article en

Abstract

The rapid development of Artificial Intelligence (AI) technology has profoundly affected the digitization process in the field of literature, and the application of AI technology in the ethical criticism of digital literary narratives has become more and more widespread.In this paper, combining RoBERTa pre-training model, BiLSTM, and attention mechanism, we constructed a sentiment analysis model based on RoBERTa-BiLSTM-Attention, and analyzed the sentiment trend of the novel text with the object of Chinese New Century Literature "The Riverbank" as a basis for the AI to generate literary narrative ethical criticism.The experimental results show that this paper's model with the multi-head attention mechanism layer improves 3.22% and 1.65% in the two evaluation indexes of accuracy and F1, respectively, compared with the RoBERTa-BiLSTM model without the addition of the multi-head attention mechanism layer, which proves the effectiveness of this paper's model in introducing the attention mechanism.Meanwhile, comparing with other models, the accuracy and F1 value of this paper's model are optimal, reaching 88.35% and 86.52%, respectively, which indicates that this paper's model is suitable for the task of recognizing the emotions of literary texts.

International journal for housing science and its applications.Vol. 46(3)
Openalex Percentile: Top 65%
Medical Research and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AI-Driven Ethical Criticism of Literary Narrative: An Example from Chinese New Century Literature · International journal for housing science and its applications. (2025) | TGRS Research Map | TGRS