Sentiment Analysis
What is Sentiment Analysis?
Sentiment analysis uses natural language processing (NLP) and machine learning to analyze text and determine whether the sentiment behind it is positive, negative, or neutral. Businesses, researchers, and policymakers use sentiment analysis to track public opinion, monitor brand reputation, and improve customer experience by understanding the emotions behind social media posts, reviews, and other text data.
The Basic Idea
Imagine that you own a small independent cafe called The Morning Nook. You’ve always experienced steady business, but for the past few months, you’ve noticed your sales have gone down. Something is off, but how can you figure out what your customers’ concerns are?
A sentiment analysis will help you understand what is being said about your cafe online and how your customers feel about your brand. While you could manually check a few Yelp and Google Reviews, a sentiment analysis that leverages machine learning tools to analyze reviews and mentions of your company will lead to a better understanding of the big picture. After all, there’s just so much narrative online!
Sentiment analysis uses natural language processing and machine learning tools to train an algorithm to understand language in a way similar to humans and interpret the sentiment of customers.1 For example, imagine the computer analyzes the following three reviews:
“I’ve always loved the coffee at The Morning Nook, but it takes too long to get my order in the morning on my way to work.”
“The Morning Nook is stuck in the past—they only offer soy milk as a dairy alternative.”
“The staff is so friendly at The Morning Nook, but the wait times have gotten ridiculous!”
The algorithm will leverage tokenization, the breaking down of text into smaller meaningful units such as “too long” and “wait times,” to make it easier to analyze sentiment. The algorithm will have been trained to designate the first and third reviews as mixed and the second as negative. It can thus assign sentiment scores to each: +0 for mixed and -1 for negative. Overall, customer sentiment is trending downwards. The sentiment analysis can also let you know what themes are popping up, such as complaints about the wait times. Understanding how your customers feel can help you make changes to address their needs. You may add more baristas during the morning rush or develop an app where customers can order for pick-up. You can also offer more variety for non-dairy options. After implementing these changes, your business returns to its normal levels. What’s more, now you know how valuable sentiment analysis can be, and you can use it as a way to monitor how your customers feel in real-time. This way, the next time customers have a concern about your business, you’ll be able to address it quickly and avoid declining sales.
“Public sentiment is everything. With public sentiment, nothing can fail. Without it, nothing can succeed.”
— Abraham Lincoln, former U.S. President2
About the Author
Emilie Rose Jones
Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible.
