If a teacher is prone to hyperbole — lots of “greats!” and “excellents!” and “A+++” grades — it’s natural for a student to perceive a mere “good” as an undesirable response. According to Panagiotis Ipeirotis, associate professor at New York University, the same perception applies to online reviews.
In a recent interview, Ipeirotis touched on the the negative impact of good-enough reviews and a host of other data-related topics. Highlights from the interview (below) included:
- Sentiment analysis is a commonly used tool for measuring what people are saying about a particular company or brand, but it has issues. “The problem with sentiment analysis,” said Ipeirotis, “is that it tends to be rather generic, and it’s not customized to the context in which people read.” Ipeirotis pointed to Amazon as a good example here, where customer feedback about a merchant that says “good packaging” might initially appear as positive sentiment, but “good” feedback can have a negative effect on sales. “People tend to exaggerate a lot on Amazon. ‘Excellent seller.’ ‘Super-duper service.’ ‘Lightning-fast delivery.’ So when someone says ‘good packaging,’ it’s perceived as, ‘that’s all you’ve got?'” [Discussed at the 0:42 mark.]
- Ipeirotis suggested that people should challenge the initial conclusions they make from data. “Every time that something seems to confirm your intuition too much, I think it’s good to ask for feedback.” [Discussed at 2:24.]
- Ipeirotis has done considerable research on Amazon’s Mechanical Turk (MTurk) platform. He described MTurk as “an interesting example of a market that started with the wrong design.” Amazon thought that its cloud-based labor service would be “yet another of its cloud services.” But a market that “involves people who are strategic and responding to incentives,” said Ipeirotis, “is very different than a market for CPUs and so on.” Because Amazon didn’t take this into consideration early on, the service has faced spam and reputation issues. Ipeirotis pointed to the site’s use of anonymity as an example: Anonymity was supposed to protect privacy, but it’s actually hurt some of the people who are good at what they do because anonymity is often associated with spammers. [Discussed at 2:55.]
The full interview is available in the following video:
Some quotes from this interview were edited and condensed for clarity.