Letting Americans Tell Us About What They Think of Politicians In Their Own Words
Using Natural Language Processing to Explore and Analyze Responses to Open-Ended Questions About Presidential Candidates
Using responses to the open-ended questions about the positive and negative attributes of the presidential candidates in the American National Election Studies (ANES), I use Natural Language Processing methods to explore how Americans evaluate the candidates, via:
- Using Bag of Words models to estimate the most popular positive and negative terms in peoples’ considerations.
- Comparing how well several supervised learning models predict the sentiment of peoples’ responses with Bag of Words and TF-IDF to vectorize the text.
- Employing multiple topic models (LDA, LDA, NNMF) to cluster the responses in the hopes of identifying labels to code individual statements.
Check out the project notebook. But, in the meantime, here are some interesting highlights.
Most Frequently Used Bigrams in Candidate Considerations
- I used Bag of Words models to find at the twenty most frequently used bigrams in Americans’ positive and negative considerations of the presidential candidates.
- The plots explore popular bigrams from evaluations of the Democratic and Republican candidates, respectively, pooling all three years.
- They also show popular bigrams of Clinton and Trump from 2016 and bigrams of Barack Obama in 2008 and 2012.
- Pooling positive and negative evaluations, I ran LSA, LDA, and NNMF topic extraction models to cluster the text together (using unigrams, bigrams, and trigrams).
- I explored a range of n’s to determine the number of topics.
- I pooled all years and then looked at 2008, 2012, and 2016 separately.
- The figures below reveal a part of this section of the project.
See the whole project here.