원문정보
초록
영어
The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.
목차
Ⅰ. Introduction
Ⅱ. Related Works
2.1. Sentiment Analysis Using RL Algorithms
2.2. Sentiment Analysis Using DRL Algorithms
2.3. Sentiment Analysis Using Hierarchical RL Algorithms
2.4. Comparative Analysis of Related Works
Ⅲ. System Methodology
3.1. Preprocessing
3.2. Environment
3.3. Agent
3.4. Reinforcement Learning algorithms
3.5. Multi-Agent RL System
3.6. Aspect Word Detection and Labeling
3.7. Sentiment Word Detection
3.8. Negation Detection
3.9. Sentiment Label Generation
Ⅳ. Experimental Results and Discussion
4.1. Description of Datasets
4.2. Experimental Setup
4.3. Hyper Parameters of the Proposed Model
4.4. Performance of the Proposed Model
4.5. The Proposed Model as a Business Problem
Ⅴ. Conclusion and Future Work
References