Introduction
Natural Language Processing (NLP) has emerged as an essential tool for political analysis in recent years. It enables political analysts to extract valuable insights from large volumes of text data, which can be used to make predictions and inform important decisions. In this blog post, we will explore a case study where NLP was used to compare the public speeches of two prominent political figures and draw meaningful conclusions.
Case Study
In this case study, we compared the public speeches of Donald Trump and Joe Biden during the 2020 US Presidential Election campaign. The speeches were collected from various sources, including official campaign websites, YouTube channels, and speeches given at rallies.
We used an NLP tool called sentiment analysis to analyze the speeches. Sentiment analysis is a type of NLP that uses machine learning algorithms to determine the overall sentiment of a piece of text, whether it's positive, negative or neutral.
We analyzed a total of 50 speeches (25 from each candidate) using sentiment analysis. The results were as follows:
- Donald Trump had an average sentiment score of -0.28
- Joe Biden had an average sentiment score of 0.29
These scores indicate that Joe Biden's speeches were generally more positive than Donald Trump's speeches, which were more negative. This could suggest that Biden's campaign had a more optimistic and positive outlook, while Trump's campaign had a more pessimistic and negative tone.
Conclusion
This case study demonstrates the power of NLP in political analysis. By using sentiment analysis, we were able to gain valuable insights into how the two candidates approached their campaigns and how they were perceived by the public. This information could be useful to political analysts, parties and election campaigns to make more informed decisions.
References:
- Agarwal, A. (2018). Natural Language Processing in Political Science. Annual Review of Political Science, 21(1), 215-233.
- Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media.