Simplifying Sentiment Analysis using VADER in Python (on Social Media Text)

An easy to use Python library built especially for sentiment analysis of social media texts.

PC:Pixabay/PDPics

“If you want to understand people, especially your customers…then you have to be able to possess a strong capability to analyze text. “ — Paul Hoffman, CTO:Space-Time Insight

The 2016 US Presidential Elections were important for many reasons. Apart from the political aspect, the major use of analytics during the entire canvassing period garnered a lot of attention. During the elections, millions of Twitter data points, belonging to both Clinton and Trump, were analyzed and classified with a sentiment of either positive, neutral, or negative. Some of the interesting outcomes that emerged from the analysis were:

• The tweets that mentioned ‘@realDonaldTrump’ were greater than those mentioning ‘@HillaryClinton’, indicating the majority were tweeting about Trump.
• For both candidates, negative tweets outnumbered the positive ones.
• The Positive to Negative Tweet ratio was better for Trump than for Clinton.