On the Challenges of Sentiment Analysis for Dynamic Events
This work identifies a problem for businesses and researchers in natural language processing seeking to analyze sentiment in real-time social media data, but it is incremental as it builds on existing sentiment analysis efforts.
The paper addresses the challenges of sentiment analysis for dynamic events, highlighting the increasing use of social media for studying political sentiment, with 44% of US adults learning about the 2016 presidential election through social media and 17.1 million tweets during the first Trump-Hillary debate.
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.