Earticle

초록

영어

Over the past few decades, natural language processing research has not made much. However, the widespread use of deep learning and neural networks attracted attention for the application of neural networks in natural language processing. Sentiment analysis is one of the challenges of natural language processing. Emotions are things that a person thinks and feels. Therefore, sentiment analysis should be able to analyze the person’s attitude, opinions, and inclinations in text or actual text. In the case of emotion analysis, it is a priority to simply classify two emotions: positive and negative. In this paper we propose the deep learning based sentimental analysis system according to various optimizer that is SGD, ADAM and RMSProp. Through experimental result RMSprop optimizer shows the best performance compared to others on IMDB data set. Future work is to find more best hyper parameter for sentimental analysis system.

목차

Abstract
1. Introduction
2. Time Series Prediction Model
2.1 RNN structure and problems
2.2 LSTM
3. Proposed model
3.1 Training and Test data
3.2 Word embedding layer
3.3 LSTM layer
3.4 Dense layer
3.5 Optimizer
4. Experiment
5. Conclusion and Future works
Acknowledgement
References

키워드

저자

• Seong Hoon Eom [ Associate Professor, Department of Electrical and Electronic Engineering, Youngsan University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380