Millions of people access Twitter every month, and the sheer volume of tweets flowing through the company’s platform is remarkable. Different companies have tried to harness the value of those tweets and derive information from the 140 character blips. But it would seem that making suggestions to users about the best book to read or movie to watch based on tweets isn’t an easy challenge.
Parakweet is a company that’s working to use natural language processing to cull through your tweets and make smart, targeted suggestions based on the data. On Friday, the company plans to announce the launch of two products. One is Bookvi.be, a consumer-oriented book recommendation engine, and TrendFinder For Movies, which is a social media dashboard primarily for entertainment companies to monitor conversations around movies. The latter is a paid product that provides the company with revenue, and the former is free for consumers.
“It’s a very hard problem we’ve tackled, which is accurately identifying sentiments,” CEO Ramesh Haridas said. “With 400 million tweets a day, there are 700,000 a day discussing movies, and if you tried text-matching techniques you’d come back with 40 million results. Many movies and books have very common titles, so you’d just drown in data.”
Both products use natural language processing to figure out how common a title is on Twitter, but also how a consumer is tweeting about a particular product, and they make recommendations based on those tweets. For instance, if I tweeted that a particular book is terrible and no one should ever read it, it would look ridiculous for a book recommendation engine to suggest that book to people. So Bookvi.be is structured to recognize the words I’m using in my tweet and know not to recommend that book. Users can choose to have a weekly email send to them with book suggestions, and they can type in their Twitter username to get book suggestions based on the people they follow.
“The bar on accuracy is very high,” Haridas said. “Especially if it’s sent via email, the precision needs to be intact.”
I’ve looked at a good number of social recommendation tools, and this one definitely stood out. For one, it was incredibly accurate — all the books it suggested were books I would actually read. But most importantly, it didn’t require me to create a new social network, or depend on friends for reviews, so you could get a lot of value from it right away. This is the obvious benefit of using someone else’s social graph, but Twitter seems perfectly suited to making content recommendations for things like books. Because unlike my Facebook friends, the people I follow on Twitter tend to accurately reflect my intellectual interests.
Of course, there are the obvious potential pitfalls of building a product around someone else’s platform, although Haridas said they support Facebook and are adding other platforms. But there’s a good deal of money to be made in accurately processing and understanding the words people are tweeting, as evidenced by Twitter’s acquisition of Lucky Sort this week, a similar company that also tries to figure out what people are talking about on social media. As I’ve written before, as Twitter ramps up its advertising products it’s more important than ever for the company to be able to provide brands with more accurate ad targeting which hinges on the words people are tweeting and searching.