【推荐】用TensorFlow实现LSTM社交对话股市情感分析

Note: Readers can access the code for this tutorial on GitHub.

Long short-term memory (LSTM) networks have been around for 20 years (Hochreiter and Schmidhuber, 1997), but have seen a tremendous growth in popularity and success over the last few years. LSTM networks are a specialized type of recurrent neural network (RNN)—a neural network architecture used for modeling sequential data and often applied to natural language processing (NLP) tasks. The advantage of LSTMs over traditional RNNs is that they retain information for long periods of time, allowing for important information learned early in the sequence to have a larger impact on model decisions made at the end of the sequence.

In this tutorial, we will introduce the LSTM network architecture and build our own LSTM network to classify stock market sentiment from messages on StockTwits. We use TensorFlow because it offers compact, high-level commands and is very popular these days.

LSTM cells and network architecture

Before we dive into building our network, let’s go through a brief introduction of how LSTM cells work and an LSTM network architecture (Figure 1).

Figure 1.

Unrolled RNN cell structure (top) vs. LSTM cell structure (bottom). Image courtesy of Christopher Olah, used with permission.

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