Oral Presentations: UC 331
Sentiment Expression on Twitter Regarding the Middle East
Presentation Type
Presentation
Faculty Mentor’s Full Name
Laure Drake
Abstract / Artist's Statement
Social media has transformed the awareness of events around the world as it allows for instant, up-to-the-second data transmission and communication for a variety of interested parties. Due to the ongoing turmoil surrounding the Middle East and its heightened media attention, I chose to research what type of emotions and interactions are found on Twitter with regard to the region and related topics. I selected Twitter due to the relative accessibility, workability, and anticipated sufficient size of data samples available. Twitter reports 1 billion created accounts with 320 million active accounts as of December 31, 2015. These active accounts, defined as a ratio of followers to followed accounts, generate roughly 500 million tweets per day from around the world. In this research, I am looking at scholarly works, journalism sources, and other reports to learn more about some of the ways Twitter has been used as it relates to the Middle East and better establish context for my data analysis. This information helps guide me in performing real-time sentiment analysis – or opinion mining – on Twitter data using open source sentiment dictionaries with machine learning algorithms to provide highly accurate analysis of emotional response as it relates to the Middle East. This sentiment analysis is performed by assigning numerical values to words to help quantify positive, neutral, or negative emotion associated with my topic. My findings will help to draw conclusions as to whether there are specific emotions correlated with the region and associated topics, the degrees of emotion felt when tweeting about specific subjects, and how spot-checked dates after different events influence the sentiment broadcast on Twitter. This unfiltered look at people’s emotions on Twitter serves to quantify how twitter users perceive the Middle East and related topics.
Category
Social Sciences
Sentiment Expression on Twitter Regarding the Middle East
Social media has transformed the awareness of events around the world as it allows for instant, up-to-the-second data transmission and communication for a variety of interested parties. Due to the ongoing turmoil surrounding the Middle East and its heightened media attention, I chose to research what type of emotions and interactions are found on Twitter with regard to the region and related topics. I selected Twitter due to the relative accessibility, workability, and anticipated sufficient size of data samples available. Twitter reports 1 billion created accounts with 320 million active accounts as of December 31, 2015. These active accounts, defined as a ratio of followers to followed accounts, generate roughly 500 million tweets per day from around the world. In this research, I am looking at scholarly works, journalism sources, and other reports to learn more about some of the ways Twitter has been used as it relates to the Middle East and better establish context for my data analysis. This information helps guide me in performing real-time sentiment analysis – or opinion mining – on Twitter data using open source sentiment dictionaries with machine learning algorithms to provide highly accurate analysis of emotional response as it relates to the Middle East. This sentiment analysis is performed by assigning numerical values to words to help quantify positive, neutral, or negative emotion associated with my topic. My findings will help to draw conclusions as to whether there are specific emotions correlated with the region and associated topics, the degrees of emotion felt when tweeting about specific subjects, and how spot-checked dates after different events influence the sentiment broadcast on Twitter. This unfiltered look at people’s emotions on Twitter serves to quantify how twitter users perceive the Middle East and related topics.