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ID 118516
Author
Ding, Fei Tokushima University
Keywords
Neuro-symbolic AI
Sentiment Analysis
Fine-tuned Transformer
Latent Dirichlet Allocation
Content Type
Journal Article
Description
For text sentiment analysis, state-of-the-art neural language models have demonstrated promising performance. However, they lack interpretability, require vast volumes of annotated data, and are typically specialized for tasks. In this paper, we explore a connection between fine-tuned Transformer models and unsupervised LDA approach to cope with text sentiment analysis tasks, inspired by the concept of Neuro-symbolic AI. The Transformer and LDA models are combined as a feature extractor to extract the hidden representations of the input text sequences. Subsequently, we employ a feedforward network to forecast various sentiment analysis tasks, such as multi-label emotion prediction, dialogue quality prediction, and nugget detection. Our proposed method obtains the best results in the NTCIR-16 dialogue evaluation (DialEval-2) task, as well as cutting-edge results in emotional intensity prediction using the Ren_CECps corpus. Extensive experiments show that our proposed method is highly explainable, cost-effective in training, and superior in terms of accuracy and robustness.
Journal Title
IEEE Transactions on Affective Computing
ISSN
19493045
Publisher
IEEE
Volume
15
Issue
2
Start Page
493
End Page
507
Published Date
2023-05-23
Remark
論文本文は2025-05-23以降公開予定
Rights
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
language
eng
TextVersion
その他
departments
Science and Technology