NaSent, which is short for Neural Analysis of Sentiment, is a program
that determines whether movie reviews are positive or negative. There
are already programs that do this, largely by counting positive and
negative words in a review, but NaSent is more sophisticated: It can
extract meaning from whole phrases and sentences, which puts it ever so
slightly closer to the realm of a real live reader.
NaSent was created by computer scientists Richard Socher, Christopher Manning, and Andrew Ng, and linguist Christopher Potts, and presented last month at a conference in Seattle. The researchers began by feeding the program 214,000 phrases and sentences from movie reviews that had been coded manually on a scale from like to dislike. NaSent then draws on that foundation to determine the meaning of unfamiliar sentences.
A news release put out last month by the Stanford Engineering Department included a sample analysis that highlights NaSent’s ability to detect nuance. The program is able to tell the difference between two sentences that contain the exact same words in nearly the same order, but have completely opposite meanings:
NaSent was created by computer scientists Richard Socher, Christopher Manning, and Andrew Ng, and linguist Christopher Potts, and presented last month at a conference in Seattle. The researchers began by feeding the program 214,000 phrases and sentences from movie reviews that had been coded manually on a scale from like to dislike. NaSent then draws on that foundation to determine the meaning of unfamiliar sentences.
A news release put out last month by the Stanford Engineering Department included a sample analysis that highlights NaSent’s ability to detect nuance. The program is able to tell the difference between two sentences that contain the exact same words in nearly the same order, but have completely opposite meanings:
- “Unlike the surreal Leon, this movie is weird but likeable.”
- “Unlike the surreal but likeable Leon, this movie is weird.”
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