Decoding Political Sentiment: How Twitter Analysis Shapes Our Understanding of Elections

Jordan Keane in Politics & Policy June 2025 • 4 min read.

"Uncover how real-time analysis of microblogging posts on Twitter enhances our understanding of political trends and voter sentiment during elections."

In today's digital age, Twitter has evolved into a pivotal platform for real-time microblogging, enabling users to share thoughts and engage with online audiences. Its mobile accessibility has amplified its popularity, transforming how we interact and disseminate information.

Over the past decade, social media platforms like Twitter have become vital venues for political campaigns and discussions. Politicians now leverage microblogging to connect with citizens, while online news portals rapidly spread political narratives, creating an environment where understanding public sentiment is more critical than ever.

Amidst the rise of political text readily available, new research questions are emerging. While studies suggest social media can forecast electoral outcomes, the potential of Twitter to gauge public opinion and sentiment remains a captivating area of exploration. In this article, we explore the intersection of Twitter data and sentiment analysis, emphasizing the nuances beyond simple positive, negative, and neutral classifications to provide deeper insights into political audiences.

Analyzing Political Sentiment on Twitter: A Deep Dive

The intersection of political science and computational text analysis has created new pathways for understanding public opinion during elections. While traditional approaches focused on identifying the polarity of online posts, modern techniques aim to leverage the impact of these posts to uncover real-time trends.

A novel approach involves a nuanced classification of sentiment polarity, considering the correlations between words, events, and sentiments. This method distinguishes between 'neutral' tweets, which convey no sentiment, and 'balanced' tweets, which mix positive and negative sentiments equally. This refined categorization allows for a more accurate understanding of public attitudes.

Comprehensive Data Collection: Gathering a substantial sample of tweets related to specific political events, such as the 2017 UK General Election.
Real-Time Analysis: Employing tools for the real-time retrieval and analysis of microblogging posts.
Sentiment Classification: Introducing a novel classification of sentiment polarity.
Correlation Analysis: Examining the relationships between words, events, and overall sentiment.

To conduct this real-time sentiment analysis, several tools and methods are used. A Java-based application interacts with the Twitter API to retrieve public tweets, using libraries such as Twitter4j. Cloud storage solutions like MongoDB Atlas are used for storing data, while platforms such as MongoDB Compass and Knowi facilitate visualization, browsing, and data analysis. This integrated approach supports the detailed examination of tweets, sentiment words, and their impact on political discourse.

Future Implications and Opportunities

In conclusion, analyzing sentiment on platforms like Twitter provides valuable insights into public opinion and engagement during political events. The correlation between neutral tweets and search volume highlights the utility of sentiment analysis tools for monitoring critical events in political campaigns. Future research can build on these findings by considering tweet context, geographical locations, and additional hashtags. Further exploration into fake news and external events will enhance the robustness of these analyses, creating a more comprehensive understanding of political discourse.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/csci.2017.253, Alternate LINK

Title: Analysing The Sentiment Expressed By Political Audiences On Twitter: The Case Of The 2017 Uk General Election

Journal: 2017 International Conference on Computational Science and Computational Intelligence (CSCI)

Publisher: IEEE

Authors: Matthew Plummer, Marco A. Palomino, Giovanni L. Masala

Published: 2017-12-01

Everything You Need To Know

Why is understanding public sentiment on microblogging platforms like Twitter crucial during elections?

Microblogging platforms, especially Twitter, have become increasingly important venues for political campaigns and discussions. Politicians use microblogging to connect with citizens, and online news portals rapidly spread political narratives. Understanding public sentiment in this environment is crucial because it provides insights into voter opinions, preferences, and potential voting behaviors, which can influence campaign strategies and policy decisions. Sentiment analysis offers a way to gauge and interpret these public sentiments effectively.

How does the novel method of sentiment polarity classification improve the accuracy of political sentiment analysis on Twitter?

A novel method of sentiment polarity classification distinguishes between 'neutral' tweets, which convey no sentiment, and 'balanced' tweets, which mix positive and negative sentiments equally. Traditional methods only classify tweets as positive, negative, or neutral, which can oversimplify public opinion. This nuanced categorization allows for a more accurate and detailed understanding of public attitudes, capturing the complexity of mixed sentiments and providing deeper insights into political discourse. Ignoring balanced sentiments can lead to skewed interpretations of public opinion.

What specific tools and methods are used to conduct real-time sentiment analysis of political tweets?

Several tools and methods are used, including a Java-based application to interact with the Twitter API via libraries like Twitter4j for retrieving public tweets. Cloud storage solutions such as MongoDB Atlas are used for storing data, while platforms like MongoDB Compass and Knowi facilitate visualization, browsing, and data analysis. This integrated approach supports the detailed examination of tweets, sentiment words, and their impact on political discourse.

What are the key steps involved in analyzing political sentiment on Twitter, and how do they contribute to understanding public opinion?

Comprehensive data collection involves gathering a substantial sample of tweets related to specific political events, such as the 2017 UK General Election. Real-time analysis employs tools for the immediate retrieval and analysis of microblogging posts. Sentiment classification introduces a novel categorization of sentiment polarity, and correlation analysis examines the relationships between words, events, and overall sentiment. These steps ensure a thorough and nuanced understanding of public sentiment, enhancing the accuracy of political analysis.

What are some potential areas for future research to enhance the accuracy and comprehensiveness of sentiment analysis in political discourse?

Future research can consider tweet context, geographical locations, and additional hashtags to enhance sentiment analysis. Further exploration into the impact of fake news and external events will also improve the robustness of these analyses. By incorporating these additional factors, sentiment analysis can provide a more comprehensive and nuanced understanding of political discourse. Ignoring these elements may result in incomplete or biased assessments of public sentiment.