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Seth Grimes

Counting Bugs at LinkedIn

by · January 27, 2015

LinkedIn has a bug problem, in two senses. There are long-standing, unresolved errors, and there are agitators like me (or is it only me?) who keep finding more and say so.

This article is my latest “bug LinkedIn” entry. My latest finds center on counting. They’re very visible. I’ll show you two instances, and toss in a screenshot of a special slip-up.


A Cheap Way to Discover Brand-Topic Affinities on Twitter

by · August 11, 2014

Sometimes interesting things appear when you’re not even looking. And some lessons taught are applicable far beyond immediate challenges.

Case in point: The realization that Twitter advertising statistics can reveal brand-topic affinities.

Ad stats help you assess how well you’ve targeted promoted tweets — that’s their purpose — but you can use them for much more. You can use them to study competitive positioning and identify influencers around particular topics of interest. The trick is to craft tweets that don’t (only) promote a product or service, but also/instead help you evaluate the topic-engagement link. The insights revealed aren’t especially useful for me — I’m well-positioned in my text and sentiment analysis consulting specialization — but if your business depends on precision online targeting, you may find ads data to be a new, unique, inexpensive source of social intelligence.


LinkedIn Misconnects Show that Automated Matching is Hard

by · July 9, 2014

I’ll report here on a LinkedIn error — it’s not a bug, it’s a flawed algorithm, significant although far from earth-shattering — that shows how difficult automated matching can be. I’ll then offer practical steps LinkedIn could take toward accurate matching.

Why should you read on? Not only because (I’m guessing) you have a LinkedIn profile, but also because in an “omni-channel” world, data matching — also known as data integration, data fusion, record linkage, and synthesis — is central to meeting everyday social and enterprise business challenges.

Two Mismatches

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My Search for Relevance on LinkedIn

by · March 21, 2014

I’m a heavy LinkedIn user, and like many of my ilk — recruiters, marketers, job seekers — I’m quick to jump to search for people-finding. That’s because exploring a social network, connection-by-connection, is beyond tedious. All but poorly-connected users will have millions in their networks, within 2 or 3 links, and manual exploration simply doesn’t scale. (Cutting connections isn’t the answer; it destroys a network’s value, which grows steeply with the number of participants according to the network effect.) Further, you can’t explore what you can’t see. Because many individuals hide their connection lists, a connection-surfer won’t see entire branches of her network. So I — and you — use search.


Social Sentiment’s Missing Measures

by · January 29, 2014

Social-analytics accuracy is essential, whether you seek broad understanding of attitudes that affect your business, early warning of emerging threats, or to spot individual issues for customer care. Yet as I wrote in 2012, “focus on accuracy distracts users from the real goal, not 95% analysis accuracy but rather support for the most effective possible business decision making.” The most accurate and sophisticated listening/response program will fail if you’re not measuring values that matter and communicating useful and usable insights.


It Goes Without Saying: From Sentiment to Intent

by · October 18, 2012

When someone remarks “It goes without saying,” they are about to explain, possibly in detail, something the listener is supposed to already  know. Similarly, a speaker “who needs no introduction” is often about to get one. When you’re negotiating with someone who says “It’s not about the money,” the sticking point is probably just that, the money, and that classic dump-your-boyfriend line, “It’s not you, it’s me,” means, “You’re not smart/good looking/caring/interesting enough for me.”


Metavana Mix: Social Complexity, SparkScore Simplicity

by · July 20, 2012

Metavana is a new-on-the-scene semantic-analysis vendor whose core science invokes a supposed universal descriptive pattern, the Maximum Information Principle. MIP, Metavana explains, describes the distribution of galaxy sizes and, as exploited by Metavana’s software, the distribution of multi-term, natural-language “n-plets.”

Interesting, but there’s plenty of computational-linguistics and semantic-science mojo in a host of established, competing text and sentiment analysis offerings, developed by smart people. The real question is this one: Does MIP make for great “solutions that measure customer satisfaction,” capable of “taming the chaos of the social Web”?


Never Trust Sentiment Accuracy Claims

by · July 17, 2012

Sentiment analysis plays a key role in social intelligence (a generalization of social-media analytics) and in customer-experience programs, but the disparity in tool performance is wide. It’s natural that users will look for accuracy figures, and that solution providers — the ones that pretend to better performance — will use accuracy as a differentiator. The competition is suspect, for reasons I outlined in Social Media Sentiment: Competing on Accuracy. Per that article, there’s no standard yardstick for sentiment-analysis accuracy measurement. But that’s a technical point. Worth further exploring:

  • Providers, using human raters as a yardstick, don’t play by the same rules.

About Seth Grimes

Seth Grimes

Seth Grimes helps companies find business value in enterprise data and online information. He consults via Alta Plana Corporation, works as an industry analyst, organizes the Sentiment Analysis Symposium and tweets at @sethgrimes.

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