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The project explores the process of human annotation, calculating inter-annotator agreement metrics and biases from 2000 Yelp Reviews annotated by 3 independent annotators on Amazon Mechanical Turk.

Dockerfile 1.28% Makefile 2.00% Python 96.72%
streamlit docker docker-compose elasticsearch mechanical-turk python seaborn

yelp-annotation-study's Introduction

Exploring Challenges in Human Data Annotation

Overview

The project explores the process of human annotation, calculating inter-annotator agreement metrics and biases from 2000 Yelp Reviews annotated by 3 independent annotators on Amazon Mechanical Turk.

Prerequisites

Make sure you have Docker and Docker Compose installed and running before proceeding.

Installation & Setup

  1. Clone the git repository to your local machine:

    git clone https://github.com/nihaldsouza/yelp-annotation-study.git

  2. Change directory into the project folder:

    cd yelp-annotation-study

  3. Run the following command:

    docker-compose up

Note: This may take a few minutes as Elasticsearch requires some time to be setup. You can proceed once you see logs from the 'streamlit' container, something similar to:

streamlit_log

  1. On your browser visit: http://localhost:8501/

  2. To gracefully shutdown the app, Control + C

yelp-annotation-study's People

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