Use Python to Analyze Text
This example is meant to provide a cursory look at the world mood according to the Twitter profiles of news agencies. In essence, the script allows one to quickly perform a sentiment analysis on the most recent tweets of any given Twitter accounts and plot the results. Notable libraries used to complete this application include: Matplotlib, Pandas, Tweepy, VADER Sentiment Analysis, and Seaborn.
Sentiment analysis is simply the process of working out (statistically) whether a piece of text is positive, negative or neutral. The majority of sentiment analysis approaches take one of two forms: polarity-based, where pieces of texts are classified as either positive or negative, or valence-based, where the intensity of the sentiment is taken into account.
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.
Chart 1a: Use Pyton to Analyze Text
Chart 1b: Codes
