Presidential Debate Twitter Sentiment Analysis Using Python and NLTK

Learn how to perform sentiment analysis on Twitter data from the Presidential Debate using Python and NLTK. Discover the process of setting up Twitter Developer access, querying tweets with the #PresidentialDebate hashtag, and applying natural language processing techniques to calculate sentiment. Explore how to use TextBlob to extract polarity and subjectivity from tweets, with polarity ranging from -1 (negative) to 1 (positive) and subjectivity from 0 (least subjective) to 1 (most subjective). Gain practical skills in leveraging sentiment analysis for market research, customer feedback analysis, and financial market insights. Access provided resources including Twitter Developer, Twepy, NLTK, and Pandas documentation to enhance your understanding and implementation of the techniques demonstrated.

Presidential Debate Twitter Sentiment Analysis using Python and NLTK