This document discusses how sentiment analysis was used to analyze public opinion during the 2012 US Presidential debates between Barack Obama and Mitt Romney. Sentiment analysis involves evaluating subjective information in text data to determine sentiment. It can analyze large amounts of data from social media and news to classify sentiment as positive, negative, or neutral. The analysis of posts during and after the debates provided insights into how the public reacted to different moments and helped analysts understand who was perceived to have won each debate. Real-time analysis also allowed campaigns to track changes in sentiment over the course of live events.

1. Presidential Debate
Sentiment Analysis
Michael Sherwood
CIT 381

2. Introduction
 Sentiment analysis is a computational study of people's
opinions, appraisals, attitudes, and emotions towards
entities, individuals, issues, events, topics, and their
attributes. It's a key technique in the world of data analysis
and plays a significant role in gauging public sentiment
during significant events such as political debates.

3. The 2012 Presidential Debates
 The 2012 presidential debates were a series of three
debates between then President Barack Obama and
Republican nominee Mitt Romney. Most analysts agreed
that Romney won the first debate while Obama took the last
two. But how did they come to these conclusions?

4. Sentiment Analysis & Opinion Mining:
The Basics
 Sentiment analysis, also known as opinion mining, involves
evaluating and interpreting subjective information in text
data using machine learning, natural language processing,
and text analysis. It's widely used in social media
monitoring, customer sentiment towards products or
services, and in broader fields like public opinion on certain
topics.

5. Sentiment Analysis in the Political
Sphere
 Understanding public sentiment is crucial in politics.
Politicians need to know what voters think about their
policies and public appearances. Sentiment analysis allows
us to quantify the public's emotions, opinions, and attitudes
at scale and in real-time, which was previously nearly
impossible.

6. Case Study - The 2012 Presidential
Debates
 Sentiment analysis can be used to analyze public opinion
during and after the debates. Potential data sources include
Twitter, Facebook, news articles, blogs, and more.
Sophisticated sentiment analysis tools can sift through this
vast amount of data and classify sentiment as positive,
negative, or neutral.

7. Understanding the Results
 After Romney's first debate, analysts might have seen a
surge in positive sentiment toward him. They could identify
specific points he made that resonated with the audience.
Similar patterns of positive sentiment and key moments
could be identified for the subsequent debates won by
Obama.

8. The Role of Real-Time Analysis
 Real-time sentiment analysis allows analysts to track
changes in sentiment during live events like debates.
Analyzing social media posts or news articles allows them
to visualize immediate public responses, providing valuable
feedback to the campaign team.

9. Limitations & Challenges
 Sentiment analysis is not without its challenges.
Understanding context, sarcasm, or the neutrality of a
statement can be difficult. Moreover, biases in the data,
noise in social media data, and the sheer volume of
information can be challenging to handle.

10. Conclusion
 Sentiment analysis and opinion mining provide valuable
insights into public opinion during political events. As these
techniques continue to evolve, their role in shaping political
strategies is expected to grow.