A python package that makes it easy to add labels to text data within a Jupyter Notebook.

Ideal use is for datasets that can be managed within a pandas dataframe. Labels are applied to the selected number of records. Timestamped annotations can be saved in a dataframe for future use in any NLP/sentiment analysis project.

Target audience

• Data practitioners
• Researchers
• Students
• Data enthusiasts

Anyone in need of a simple and intuitive product to label text data easily and efficiently will benefit from tortus.

Installation

Run the following to install:

pip install tortus
jupyter nbextension enable --py widgetsnbextension

Usage

Import the necessary modules into a Jupyter Notebook.

import pandas as pd
from tortus import Tortus

Read your dataset into a pandas dataframe.

movie_reviews = pd.read_csv('movie_reviews.csv')

Create an instance of Tortus class. You are required to enter the dataframe and the name of the column of the text to be annotated. Optional parameters include num_records, id_column, annotations, random and labels.

tortus = Tortus(movie_reviews, 'reviews', num_records=3, id_column='review_id')

Call the annotate method to begin annotations.

tortus.annotate()

At any time, annotations can be stored into an object. This can be passed to annotations if further annotations are required at a later time.

annotations = tortus.annotations

Example

Click here to see a sample project using tortus.