Sento Processing

This project is part of Sento's backend.

A Natural Language Processing tool designed to perform sentiment analysis on tweets and store the results obtained.

Table of contents

Prerequisites

Sento Processing requires a spaCy model that categorizes text based on the possible sentiment that it holds. The model must return the probabilities of three categories:

• P for positive sentiment.
• N for negative sentiment.
• NEU for neutral sentiment.

If you wish to create a spaCy model that can perform this task, in the sento_processing/training directory you have some scripts used to develop a model for performing sentiment analysis of tweets written in spanish. Treat them as an example that may be outdated, as the spaCy team could have changed their API and those scripts may no longer work. You can store your models in the models directory in the repository root, this directory will be ignored by git.

Processing requires a PostgreSQL database initialised previously using the instructions available in Sento API's readme.

Executing the tool

Choosing your environment

You have two options:

• Running the Docker container using Docker Compose: docker-compose up -d. You need the following software installed:

  • Docker Engine 17.12.0 or greater required.
  • Docker Compose 1.18.0 or greater required.

• Running locally on your machine, requiring:

  • Python 3.7 or greater.
  • Pipenv.

Configuring the tool

Create a config.ini file from a copy of config.example.ini and adjust the configuration according to your needs. If you use the PostgreSQL container provided in Sento API instructions, let the default value of sento-db in the [postgres].host section of your config.ini.

Running the tool

• With Docker: run docker-compose up -d, this will compile the container image for you if you have not done it previously. If you make any changes to your config.ini after running the container you will need to stop it, remove it and recreate the container's image before creating another container instance. This container will try to connect to the sento-net Docker network, where the PostGIS container is listening for connections.

• Running locally:

  • Install the necessary dependencies in a virtual environment with pipenv sync.
  • Run the following command pipenv run sento_processing/main.py, this will start the tool.

Acknowledgements

The training and evaluation datasets used for creating models for Sento Processing come from the Spanish Society for Natural Language Processing (SEPLN, in Spain). They were made for the Workshop on Semantic Analysis at SEPLN (TASS, in Spain). You can download them from their official website.

License

The source code of this project is licensed under the GNU Affero General Public License v3.0.