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Measuring sentiment can provide a snapshot of how customers feel about companies, products, or services. It’s important for organizations to be aware: 86% of people say that authenticity is a key factor when deciding what brands they like and support. In an Edelman survey, 81% of consumers said that they need to be able to trust a brand in order to buy products from them.

While sentiment analysis technology has been around for a while, researchers at the University of Maryland’s Robert H. Smith School of Business claim to have improved upon prior methods with a new system that leverages machine learning. They say that their algorithm, which sorts through social media posts to understand how people perceive brands, can comb through more data and better measure favorability.

Sentiment analysis isn’t a perfect science, but social media provides rich signals that can be used to help shape brand strategies. According to CommSights, 46% of people have opted to use social media in the past to extend their complaints to a particular company.

“There is a vast amount of social media data available to help brands better understand their customers, but it has been underutilized in part because the methods used to monitor and analyze the data have been flawed,” Wendy W. Moe, University of Maryland associate dean of master’s programs, who created the algorithm with colleague Kunpeng Zhang, said in a statement. “Our research addresses some of the shortcomings and provides a tool for companies to more accurately gauge how consumers perceive their brands.”

Algorithmic analysis

Zhang’s and Moe’s method sifts through data from posts on a brand’s page, including how many users have expressed positive or negative sentiments, “liked” something, or shared something. It predicts how people will feel about that brand in the future, scaling to billions of pages of user-brand interaction data and millions of users.

The algorithm specifically looks at users’ interactions with brands to measure favorability — whether people view that brand in a positive or negative way. And it takes into account biases, inferring favorability and measuring social media users’ positivity based on their comments in the user-brand interaction data.

Zhang and Moe say that brands can apply the algorithm to a range of platforms, such as Facebook, Twitter, and Instagram, as long as the platforms provide user-brand interaction data and allow users to comment, share, and like content. The algorithm importantly doesn’t use private information, like user demographics, relying instead on user-brand publicly available interaction data.

“A brand needs to monitor the health of their brand dynamically,” Zhang said in a statement. “Then they can change marketing strategy to impact their brand favorability or better respond to competitors. They can better see their current location in the market in terms of their brand favorability. That can guide a brand to change marketing [practices].”

Zhang’s and Moe’s research is detailed in the paper “Measuring Brand Favorability Using Large-Scale Social Media Data,” which will be published in the forthcoming issue of the journal Information Systems Research.

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