Improving NLTK Sentiment Analysis with Data Annotation

Jayson DeLancey

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29 May 2020CPOL6 min read

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This article is the sixth in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. In this article let’s look at what a process of annotating our own dataset would entail.

We’ll build a library to help us label and identify features in Reddit comments to improve the accuracy of a Natural Language Toolkit (NLTK) VADER sentiment analysis with a machine learning approach.

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This article is part of the series 'Sentiment Analysis ◁ Prev View All Next ▷

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

Written By

Jayson DeLancey

Technical Lead

United States

Jayson manages Developer Relations for Dolby Laboratories, helping developers deliver spectacular experiences with media.

Jayson likes learning and teaching about new technologies with a wide range applications and industries. He's built solutions with companies including DreamWorks Animation (Kung Fu Panda, How to Train Your Dragon, etc.), General Electric (Predix Industrial IoT), The MathWorks (MATLAB), Rackspace (Cloud), and HERE Technologies (Maps, Automotive).

Improving NLTK Sentiment Analysis with Data Annotation

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