This repository contains the iPython notebook and training data to accompany the O'Reilly tutorial on sentiment analysis with LSTMs in Tensorflow. See the original tutorial to run this code in a pre-built environment on O'Reilly's servers with cell-by-cell guidance, or run these files on your own machine. There is also another file called Pre-Trained LSTM.ipynb which allows you to input your own text, and see the output of the trained network.
Before running the notebook, you'll first need to download all data we'll be using. This data is located in the models.tar.gz and training_data.tar.gz tarballs. We will extract these into the same directory as Oriole LSTM.ipynb. As always, the first step is to clone the repository.
git clone https://github.com/adeshpande3/LSTM-Sentiment-Analysis.gitNext, we will navigate to the newly created directory and run the following commands.
tar -xvzf models.tar.gz
tar -xvzf training_data.tar.gzIn order to run the iPython notebook, you'll need the following libraries.
- TensorFlow version 1.0 or later
- NumPy
- Jupyter
- matplotlib
The easiest way to install TensorFlow as well as NumPy, Jupyter, and matplotlib is to start with the Anaconda Python distribution.
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Follow the installation instructions for Anaconda Python. We recommend using Python 3.6.
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Follow the platform-specific TensorFlow installation instructions. Be sure to follow the "Installing with Anaconda" process, and create a Conda environment named
tensorflow. -
If you aren't still inside your Conda TensorFlow environment, enter it by opening your terminal and typing
source activate tensorflow -
If you haven't done so already, download and unzip this entire repository from GitHub, either interactively, or by entering
git clone https://github.com/adeshpande3/LSTM-Sentiment-Analysis
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Use
cdto navigate into the top directory of the repo on your machine -
Launch Jupyter by entering
jupyter notebook
and, using your browser, navigate to the URL shown in the terminal output (usually http://localhost:8888/)