Analytics

Stanford researchers to open-source model they say has nailed sentiment analysis

04th Oct `13, 02:52 PM in Analytics

Stanford Ph.D. student Richard Socher appreciates the work Google and others are doing to build neural networks that…

BDMS
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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.

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