Research on Emotional Expression in Ancient Poetry Based on Sentiment Analysis

DOI

10.2991/978-2-38476-319-1_9How to use a DOI?

Keywords

Ancient poetry; sentiment analysis; word frequency analysis

Abstract

To explore the patterns of emotional expression in ancient poetry, sentiment analysis techniques were applied to systematically study 200 poems from the Tang to Qing dynasties. Methods such as data acquisition, word frequency analysis, and sentiment classification were used. The results showed that poems expressing homesickness had the highest classification accuracy at 90%, while sadness and joy had accuracies of 87% and 89%, respectively. Through the optimization of models such as Support Vector Machines and Convolutional Neural Networks, the latter performed best with a learning rate of 0.005, achieving an accuracy of 89%. This study provides new technical means for the digital interpretation of emotional expression in ancient poetry.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qizhen Ni
AU  - Yanbin Ni
PY  - 2024
DA  - 2024/12/14
TI  - Research on Emotional Expression in Ancient Poetry Based on Sentiment Analysis
BT  - Proceedings of the 2024 6th International Conference on Literature, Art and Human Development (ICLAHD 2024)
PB  - Atlantis Press
SP  - 76
EP  - 83
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-319-1_9
DO  - 10.2991/978-2-38476-319-1_9
ID  - Ni2024
ER  -