Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models
Keito Inoshita, Xiaokang Zhou, Shohei Shimizu · 2024
The advancement of Natural Language Processing (NLP) technology has led to a focus on language models (LMs) with a general-purpose, which have achieved greater accuracy than earlier methods. However, specialized LMs still face challenges, particularly in domains with limited resources. To address this issue, this paper proposes a Multi-Domain and Multi-View oriented Deep Neural Network (MDMV-DNN) model for sentiment analysis in Large Language Models (LLMs). A novel architecture, which incorporates a Domain-Integrated Residual Attention enhanced BERT (DIRA-BERT) mechanism for multi-domain integration, and a Multi-Filter Convolutional Neural Network (MF-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) enhanced fusion mechanism for Multi-View feature extraction, is introduced and constructed. The DIRA-BERT can enhance the classification accuracy by integrating multiple expert BERT models tailored to specific tasks, thus providing refined and interpretable feature representations. The integration of MF-CNN and Bi-LSTM can further strengthen the learning ability in capturing the local key and sequential features respectively, so as to enable a comprehensive sentiment analysis through multi-view feature extraction. Experiment results demonstrate that the proposed MDMV-DNN consistently outperforms the traditional methods using common BERT models across all evaluation metrics, showcasing its effectiveness in specialized sentiment analysis tasks.