Sitemap
Analytics Vidhya

Analytics Vidhya is a community of Generative AI and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Member-only story

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

8 min readSep 23, 2018

--

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 ‘@realDonaldTrumpwere 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.

--

--

Analytics Vidhya
Analytics Vidhya

Published in Analytics Vidhya

Analytics Vidhya is a community of Generative AI and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Parul Pandey
Parul Pandey

Written by Parul Pandey

Prev - Principal Data Scientist @H2O.ai | Author of Machine Learning for High-Risk Applications