Sentiment Analysis on Social Media Platforms

Abstract

Sentiment analysis is emerging as a tool that businesses can use to monitor public opinion about their brand. Social media sites such as Twitter provide rich, varying sources of sentiment data to analyze. If social network users conform to sentiments they are exposed to, then businesses can manipulate sentiment on social media to their advantage. In my thesis, I present the codes I developed using Python and Tweepy to gather tweets about the trending topic Standing Rock, explain how sentiment analysis was performed on this data using Semantria, and demonstrate how visualization of sentiment analyses with Tableau can easily illustrate patterns and themes. The results show consistently positive-leaning sentiment among a growing network of users in the absence of an external shock, suggesting that users indeed conform to a convergent sentiment on Twitter. The potential power of sentiment analysis as a business intelligence tool can be applied if firms monitor and analyze social media sentiment to capitalize on existing products and new opportunities.