Author: Zen and the Art of Computer Programming
1 Introduction
In previous technical blogs, we have given an overview of deep learning models. With the increasing application of deep learning in text processing, computer vision and other fields, a series of new emotional analysis models based on neural networks have emerged in recent years. This article reviews these models and dissects them from both theoretical and practical perspectives.
We can divide sentiment analysis models into the following three categories:
1) Rule-based methods: such as dictionary method, regular expression method, classifier method, etc.;
2) Based on statistical methods: such as naive Bayesian method, maximum entropy model, support vector machine, etc.;
3) Neural network-based methods: including convolutional neural network (CNN), recurrent neural network (RNN), recurrent neural network (LSTM), etc.
This article focuses on the third class of models — Recurrent Neural Networks (RNNs). RNN plays a pivotal role in the field of natural language processing. It can capture sequence information and use it to model text data, so it is very suitable for text sentiment analysis. Next, we will introduce the relevant knowledge points of the RNN model.
2. Explanation of basic concepts and terms
2.1 Recurrent Neural Network (RNN)
An RNN is a special type of neural network that takes time-series data as input, passes information through hidden layers, and outputs a result. The network has a memory function, which can record the results of the previous calculation and help the current calculation better adapt to the new input. A key feature of RNNs is the ability to capture long-term dependencies in time series data. In other words, RNNs can use historical data to infer future data.
The structure of RNN consists of two parts: