التفاصيل البيبلوغرافية
| العنوان: |
An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention |
| المؤلفون: |
Kejian Liu, Yuanyuan Feng, Liying Zhang, Rongju Wang, Wei Wang, Xianzhi Yuan, Xuran Cui, Xianyong Li, Hailing Li |
| المصدر: |
Electronics; Volume 12; Issue 15; Pages: 3274 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
deep learning, personality recognition, sentiment classification, BiLSTM, self-attention, big five |
| الوصف: |
While user-generated textual content on social platforms such as Weibo provides valuable insights into public opinion and social trends, the influence of personality on sentiment expression has been largely overlooked in previous studies, especially in Chinese short texts. To bridge this gap, we propose the P-BiLSTM-SA model, which integrates personalities into sentiment classification by combining BiLSTM and self-attention mechanisms. We grouped Weibo texts based on personalities and constructed a personality lexicon using the Big Five theory and clustering algorithms. Separate sentiment classifiers were trained for each personality group using BiLSTM and self-attention, and their predictions were combined by ensemble learning. The performance of the P-BiLSTM-SA model was evaluated on the NLPCC2013 dataset and showed significant accuracy improvements. In particular, it achieved 82.88% accuracy on the NLPCC2013 dataset, a 7.51% improvement over the baseline BiLSTM-SA model. The results highlight the effectiveness of incorporating personality factors into sentiment classification of short texts. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Artificial Intelligence; https://dx.doi.org/10.3390/electronics12153274 |
| DOI: |
10.3390/electronics12153274 |
| الاتاحة: |
https://doi.org/10.3390/electronics12153274 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.56802895 |
| قاعدة البيانات: |
BASE |