Comparison of Neural Network Techniques for Text Data Analysis

다국어 초록 (Multilingual Abstract)

Generally, sequential data refers to data having continuity. Text data, which is a representative type of unstructured data, is also sequential data in that it is necessary to know the meaning of the preceding word in order to know the meaning of the following word or context. So far, many techniques for analyzing sequential data such as text data have been proposed. In this paper, four methods of 1d-CNN, LSTM, BiLSTM, and CLSTM are introduced, focusing on neural network techniques. In addition, by using this, IMDb movie review data was classified into two classes to compare the performance of the techniques in terms of accuracy and analysis time.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. NEURAL NETWORK TECHNIQUES
• 2.1 1-dimensional convolutional neural network
• 2.2 Long short-term memory
• 2.3 Bidirectional long short-term memory
• 2.4 Convolutional long short-term memory
• 3. COMPARISON VIA IMDB DATA
• 3.1 1-dimensional convolutional neural network
• 3.2 Long short-term memory
• 3.3 Bidirectional long short-term memory
• 3.4 Convolutional long short-term memory
• 4. DISCUSSION
• REFERENCES