A Deep-Learning-Driven Study on the Reception of Translated Works:A Case Study of Sentiment Analysis for Readers'Reviews on The Analects Translated by D.C.Lau
This study improves the deep pyramid convolutional neural network by using a dataset of 317.8 million words from book reviews(Amazon Review Polarity)to train the deep learning model.The enhancement increased the classification accuracy of sentiment analysis for reader reviews to 94.69％,providing a more scientific research design and approach for assessing the overseas reception of translated works.Using the reader reviews of D.C.Lau's translation of The Analects on Amazon as a case study,we find that sentiment scores calculated by the new model can more accurately reflect readers'true attitudes compared to star ratings.The overall reception of this translation was specified as 64.22％positive reviews,19.72％neutral reviews,and 16.06％negative reviews.Evaluating the reception of translated works should not rely solely on star ratings but should include multiple dimensions such as the content of the translation,overall translation quality,para-texts,and publication quality.
Digital-Intelligent Humanities(DlH)deep learningThe Analectssentiment analysis for online reviewstopic modeling
NETL
NSTL
万方数据
