There is a wealth of information in a LinkedIn profile. You can tell a lot about someone and how well they are suited to a role by analyzing their profile on LinkedIn, and let’s face it, LinkedIn in the number one platform to showcase yourself to potential employers and recruiters.
However, there are a number of issues that arise in relying on Linkedin profiles to understand a candidate’s suitability for a role and their current job function.
We set out to find out what section of a LinkedIn profile contains the most insight into an individual’s job function by using Semantic Labeling to try and predict an individual’s job title based on the info they have on their profile.
How did we do it?
We scraped and parsed a number of well known LinkedIn profiles. Using the information we extracted from the profile such as keywords, summaries, job titles and skills we attempted to predict an individual’s job function from each information section to understand which best represents an individual’s ability or function.
We started out by choosing 4 general tags or labels for an individual’s profile that would point towards their high-level job function:
- Information Technology
Using the Semantic Labeling feature to check how related a tag or label, like Marketing, was to an individual’s actual job function, we could essentially predict what an individual’s actual function is.
Our findings are displayed in the sheet embedded below. The first section of the sheet contains the profiles and information extracted. The Yellow section is the prediction results based on the skills section, red is the Summary section and Green is the Job Title results.
When a label/job function is assigned following our analysis it is also accompanied by a confidence score, which indicates how confident we are in the results. This is important to note as we dive into some of the results. The “winning” results with the highest scores are marked in green.
For this blog, we kept the functions quite general but you can get quite specific as we have with Gregory’s account below.
Scraped information and Results
But what section of a profile provides the most insight?
When analyzing a Linkedin profile or even using the search feature we’re primarily focusing on keywords mentioned in the content of that profile. Educational institutes, companies, and technologies mentioned for example.
Relying on keywords can often cause problems, there is huge a amount of latent information in a profile that is often overlooked when scanning profiles for keywords. A major problem with keyword search is that it misses related skills, e.g. someone might have “Zend Framework” on their profile, but not PHP – which is inherent, ‘cause Zend is a PHP framework. Good recruiters or somenone with programming knowledge would know this, average recruiters, however, may not.
The same could be said for someone who mentions Image Processing in their profile there is no obvious connection to other inherent knowledge such as Facial Recognition. A knowledge base such as Wikipedia, DBpedia or Freebase can be used to discover these latent connections:
Relying on job titles can also cause problems. They can be inaccurate, misleading or even made up. Especially today, as people often create their own titles. Take Rand Fishkin’s profile on LinkedIn as an example. Unless you know of MOZ and Rand’s wizardry you would have no idea he is at the forefront of Inbound, Social and SEO.
Another good example is Dharmesh Shah, founder of HubSpot’s profile. Dharmesh’s title is Founder and CTO of HubSpot. Running our analysis on the extracted title, Information Technology with a score of .016 is the job function returned for Dharmesh, which is somewhat accurate. However, running the same analysis on his skills section gives a far more accurate result suggesting Dharmesh is actually a Marketer with a “winning” score of .23.
A profile Summary can be quite insightful and can provide a strong understanding of someone’s ability and function, but the problem is they aren’t always present or they often contain very little information causing them to be overlooked or disregarded. As was the case in many of the example profiles we used.
The ones that do have a detailed summary provided some strong results. With Rand Fishkin’s profile summary returning some accurate results of Marketing and a score of .188.
There was one section that outperformed the others when providing relevant tags and confidence scores.
The Skills section on a LinkedIn profile is a gold mine of insight. Based on the information extracted from the skills section, we could more accurately predict an individual’s job function.
Comparing the results and labels assigned across all the information sections and on every profile we used, the Skills section produced the most accurate relationships and the highest confidence scores, which can be seen marked green in the sheets above.
We don’t have an exact science or formula for deciding whether a label is accurate or not, however, our experiment still does a good job of highlighting the fact that, a lot more information and insight can be gleaned from the skills section of a linkedIn profile in deciding at first glance, or automatically how well a candidate is suited to a particular job function. We will explore these ideas in future posts.