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Sentiment Analysis with LSTMs in Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 1005 |
| 2026-07-25 | 1005 |
| 2026-07-28 | 1005 |
| 2026-07-30 | 1005 |
| 2026-08-06 | 1005 |
Today
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Momentum
0.0
growth rate 0.00%/day
# Sentiment Analysis with LSTMs
This repository contains the iPython notebook and training data to accompany the [O'Reilly tutorial](https://www.oreilly.com/learning/perform-sentiment-analysis-with-lstms-using-tensorflow) 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.
## Downloading Data
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.
```bash
git clone https://github.com/adeshpande3/LSTM-Sentiment-Analysis.git
```
Next, we will navigate to the newly created directory and run the following commands.
```bash
tar -xvzf models.tar.gz
tar -xvzf training_data.tar.gz
```
## Requirements and Installation
In order to run [the iPython notebook](Oriole-LSTM.ipynb), you'll need the following libraries.
* **[TensorFlow](https://www.tensorflow.org/install/) version 1.1 (See below for later versions)**
* [NumPy](https://docs.scipy.org/doc/numpy/user/install.html)
* [Jupyter](https://jupyter.readthedocs.io/en/latest/install.html)
* [matplotlib](https://matplotlib.org/)
### TensorFlow 1.2 and later
In order to load the models without errors you need to convert the checkpoints using the converter provided by TensorFlow:
```bash
wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py
python checkpoint_convert.py models/pretrained_lstm.ckpt-90000 converted-checkpoints/pretrained_lstm-90000.ckpt
```
You should also replace the original models folder if you don't want to modify the code:
```bash
rm -rf models
mv converted-checkpoints models
```
### Docker
With Docker, you could just mount the repository and exec it.
1. Install Docker. Follow the [docker guide](https://docs.docker.com/get-started/#prepare-your-docker-environment).
2. Build docker image
``` bash
cd LSTM-Sentiment-Analysis
docker build -t="@yourname/tensorflow_1.1.0_py3" .
```
3. Run the container from the image
``` bash
docker run -p 8888:8888 --name=tensorflow_yourname_py3 -v /@YourDir/LSTM-Sentiment-Analysis:/LSTM-Sentiment-Analysis -it @yourname/tensorflow_1.1.0_py3
```
and visit the URL(http://localhost:8888/)
4. Stop and restart the container
``` bash
docker stop tensorflow_yourname_py3
docker start tensorflow_yourname_py3
docker attach tensorflow_yourname_py3
```
If jupyter is down, relaunch it by using the command below.
``` bash
cd LSTM-Sentiment-Analysis
jupyter notebook --ip=0.0.0.0 --allow-root
```
### Installing Anaconda Python and TensorFlow
The easiest way to install TensorFlow as well as NumPy, Jupyter, and matplotlib is to start with the Anaconda Python distribution.
1. Follow the [installation instructions for Anaconda Python](https://www.continuum.io/downloads). **We recommend using Python 3.6.**
2. Follow the platform-specific [TensorFlow installation instructions](https://www.tensorflow.org/install/). Be sure to follow the "Installing with Anaconda" process, and create a Conda environment named `tensorflow`.
3. If you aren't still inside your Conda TensorFlow environment, enter it by opening your terminal and typing
```bash
source activate tensorflow
```
4. If you haven't done so already, download and unzip [this entire repository from GitHub](https://github.com/adeshpande3/LSTM-Sentiment-Analysis), either interactively, or by entering
```bash
git clone https://github.com/adeshpande3/LSTM-Sentiment-Analysis
```
5. Use `cd` to nExcerpt of 4,242 characters
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Adit Deshpande · Verse Medical · United States
17
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:27c2e0d4b0260fb7, topic:sentiment-analysis, name:sentiment analysis, desc:sentiment analysis
matched fp:27c2e0d4b0260fb7, topic:tensorflow