On the Challenges of Sentiment Analysis for Dynamic Events

TL;DR

This paper discusses the difficulties in applying sentiment analysis to dynamic social media events, highlighting challenges and the importance of understanding real-time online sentiment during significant events.

Contribution

It identifies key challenges in sentiment analysis for evolving events and emphasizes the need for specialized methods to handle dynamic social media data.

Findings

Twitter reflects offline political sentiment accurately

Social media is a vital source for political information

Real-time sentiment analysis faces unique challenges

Abstract

With the proliferation of social media over the last decade, determining people's attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called sentiment and emotion analysis. For instance, businesses routinely look to develop systems to automatically understand their customer conversations by identifying the relevant content to enhance marketing their products and managing their reputations. Previous efforts to assess people's sentiment on Twitter have suggested that Twitter may be a valuable resource for studying political sentiment and that it reflects the offline political landscape. According to a Pew Research Center report, in January 2016 44 percent of US adults stated having learned about the presidential election through social media. Furthermore, 24 percent reported use of social media posts of the two candidates as a source of news and information, which is more than the 15 percent who have used both candidates' websites or emails combined. The first presidential debate between Trump and Hillary was the most tweeted debate ever with 17.1 million tweets.