Originally posted on Gigaom:
Stanford Ph.D. student Richard Socher appreciates the work Google and others are doing to build neural networks that can understand human language. He just thinks his work is more useful — and he’s going to share his code with anyone who wants to see it.
Along with a team of Stanford researchers that includes machine learning expert and Coursera co-founder Andrew Ng, Socher has developed a computer model that can accurately classify the sentiment of a sentence 85 percent of the time. The previous state of the art for this task — essentially, discerning whether the overall tone of a sentence is positive or negative — peaked at about 80 percent accuracy. In a field where improvements usually come fractions of a percent at a time, that 5 percent jump is a big deal.
It’s also a big deal to businesses, which are trying harder than ever to automate the task of figuring out what people are saying about them online. Almost every tweet, review, blog post or other piece of content expresses an opinion, but employing a human being to scan every one and instigate some sort of response or enter them into a database isn’t exactly efficient. Early approaches to sentiment analysis or social media monitoring have been kind of crude, often focusing on individual words that don’t account for context at all.