LSTM-Sentiment-Analysis

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LSTM-Sentiment-Analysis is a Data & Analytics tool that provides resources for sentiment analysis using LSTMs in Tensorflow. It includes an iPython notebook and training data for custom text analysis.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is LSTM-Sentiment-Analysis?

LSTM-Sentiment-Analysis is a GitHub repository offering resources for performing sentiment analysis with Long Short-Term Memory (LSTM) networks in Tensorflow. It provides an iPython notebook, training data, and a pre-trained LSTM model, enabling users to analyze sentiment from custom text inputs. The repository is designed to accompany an O'Reilly tutorial, making it suitable for those learning about or implementing sentiment analysis. It includes instructions for setting up the environment with TensorFlow, NumPy, Jupyter, and matplotlib, as well as guidance for Docker users. This tool is ideal for developers and data scientists looking to implement or experiment with LSTM-based sentiment analysis.

Best used for

Ideal for developers and data scientists who need to implement sentiment analysis using LSTMs, learn about TensorFlow, and experiment with pre-trained models. Especially valuable for those following the accompanying O'Reilly tutorial or integrating sentiment analysis into their projects.

Common actions

analyze sentiment

implement LSTMs

learn TensorFlow

process text data

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Capabilities

Key features

LSTM sentiment analysis
TensorFlow integration
iPython notebook
Pre-trained model
Training data included
Docker support

Target Audience

developerdata scientiststudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements to run LSTM-Sentiment-Analysis?

To run the iPython notebook, you will need TensorFlow version 1.1 (or later with conversion), NumPy, Jupyter, and matplotlib. Instructions are provided for installation via Anaconda Python, and Docker support is also available for easier setup.

How do I use my own text for sentiment analysis with this tool?

The repository includes a file named 'Pre-Trained LSTM.ipynb' which allows you to input your own text. This notebook then uses the trained network to provide sentiment analysis output for your custom input.

Is there a pre-built environment available for this project?

Yes, the original O'Reilly tutorial, which this repository accompanies, offers a pre-built environment. This allows users to run the code with cell-by-cell guidance directly on O'Reilly's servers without local setup.

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