원문정보
한국인터넷방송통신학회
International Journal of Internet, Broadcasting and Communication
Vol.14 No.1
2022.02
pp.142-151
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.
목차
Abstract
1. Introduction
2. Theory
2.1 XAI (Explainable AI)
3. XAI-based sentiment analysis system model
3.1 Experimental dataset
3.2 Model Training
4. Interpretation of explanation based on the proposed model
4.1 LIME Explanations of News group dataset
4.2 SHAP based Explanations of IMDB dataset
5. Conclusions
Acknowledgement
References
1. Introduction
2. Theory
2.1 XAI (Explainable AI)
3. XAI-based sentiment analysis system model
3.1 Experimental dataset
3.2 Model Training
4. Interpretation of explanation based on the proposed model
4.1 LIME Explanations of News group dataset
4.2 SHAP based Explanations of IMDB dataset
5. Conclusions
Acknowledgement
References
저자정보
참고문헌
자료제공 : 네이버학술정보