The Internet is full of public opinions, both expert and novice. Yelp, Facebook, Twitter, Tumblr, and other social media outlets curate public opinion to inform other patrons in the decision-making process. Many of these communications take the form of reviews, ratings, and recommendations – including those on local government services. Each of these opinions is a potential unstructured data point. Sentiment analysis, also called opinion mining, seeks to make sense of this broad landscape of informal opinions.

Sentiment analysis software automatically extracts opinions from a piece of text. Review aggregators like Rotten Tomatoes and Metacritic determine whether reviews are positive or negative and, in some cases, assign a score based on tone. Scores can then be aggregated to give a general sense of critical and public opinion of the movie, restaurant, game, or other product. In typical reviews, feedback on restaurant quality may be classified in terms of food quality, portion size, atmosphere, staff friendliness, and more. Some government services that resemble traditional customer service interactions, like permitting applications, can be evaluated along similar lines.

But not all government services feel like familiar customer service relationships, and feedback on them might not look like a product review. In some cases, residents only notice the service when it is late or lacking, like when trash and recycling are left uncollected or their streets go unplowed. Others evoke overwhelmingly positive responses, like fire and emergency medical services.

Washington D.C., is the first municipal government to adopt a public-facing sentiment analysis initiative, Grade.DC.gov, and their Fire and EMS (FEMS) have received uniformly positive monthly grades. Naturally, feedback from those whose loved ones, homes, and neighborhoods have been saved by FEMS has been overwhelmingly positive. It is important to remember that resident reviews of government services are not the only, objective measure of agency quality, but a valuable reflection of public perception.

Of course, government services are already reviewed on comment cards or surveys. Sentiment analysis does more than simply confirm or shatter previously held assumptions about public opinion. This new approach expands the number and types of people involved in giving feedback.

 Social media comments may not yet provide a comprehensive sample of public opinion, but they do capture the voices of groups whose voices have not always been engaged by traditional feedback forums. According to Pew Research, 15% of American adult Internet users also use Twitter. Urban residents use Twitter at a higher rate, with young adults (26%) and African-Americans (28%) tweeting substantially more. While home broadband adoption has stalled in the last three years, smartphone ownership has jumped 20% since 2011, with a majority of Americans now carrying devices connected to the Internet.

By mining social media, governments can harness feedback from residents even when the residents don’t reach out directly to the government. One challenge, then, is for cities to improve the quality and volume of feedback by encouraging their residents to think of social media as channels to communicate with and about government. Finally, cities need to fulfill the promise of listening to these reviews by responding to constructive feedback and improving their services.

Our project at Harvard Kennedy School, Data-Smart City Solutions, is researching and writing about issues like this every week. We are highlighting success stories of cities using data analytics and civic engagement technology to transform the way they operate and the way they engage their citizens. For more details on this and related topics, visit our website.

 

 

The #LocalGov Technology Alliance is an Esri-ICMA initiative to explore the world of big data, open data, apps and dashboards, and what it all means for local governments. For more resources to help navigate the complex world of technology, go to icma.org/localgovtechalliance.