As Jason has reported before here, sentiment analysis is a tricky thing. Even humans disagree on sentiment 15 percent of the time, so how can a computer create something more accurate? As technology evolves, sentiment analysis gets better, or so we’d like to think.
I caught up with Seth Grimes recently. He is an analytics strategist with Washington, D.C.-based Alta Plana Corporation and a contributing editor at TechWeb’s InformationWeek. He is also perhaps the leading industry analyst covering text analytics. Seth consults, writes, and speaks on business intelligence, data management and analysis systems, text mining, visualization, and related topics. With such an expert on the subject with my reach, I asked him the following:
In layman’s terms, how would you describe/define sentiment analysis?
Sentiment analysis is a set of methods, typically (but not always) implemented in computer software, that detect, measure, report, and exploit attitudes, opinions, and emotions in online, social, and enterprise information sources. (As an aside, what makes it “analysis” is that you’re doing it systematically, with some goal in mind.)
I’ll add is that sentiment analysis much more than simplistically subtracting the number of “negative” words from the number of “positive” in a document or message in order to produce a score.
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