How to extract keywords from Lever Job Applications using generative AI
How to Extract Keywords from Lever Job Applications Using Generative AI
As a hiring manager, it can be overwhelming to sift through hundreds of job applications to find the right candidates. Fortunately, there is a way to simplify the process using generative AI to extract keywords from Lever job applications. In this post, we’ll show you how to use NLP analysis to identify the most important keywords from job applications in Lever.
What is Keyword Extraction?
Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. You can use it to extract key information and themes from text and has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.
Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases and can be trained on a labeled dataset of text.
You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.
Example Use Cases
Use cases for extracting keywords from Lever job applications include:
- Identifying top skills and qualifications needed for a position
- Shortlisting candidates based on relevant experience and education
- Identifying common keywords in successful applications
- Improving the efficiency of the hiring process
- Identifying areas for training and improvement in the hiring process
Teams that might find these use cases helpful include: recruiting, hiring managers, talent acquisition, and HR.
Accessing Lever Job Applications for Analysis
You can access your job applications data in Lever by using the Lever API, exporting it in CSV format, or querying a list of applications from your data warehouse or BI tool. Once you have the data, you can use generative AI tools to extract the most important keywords from the applications.
For more information on the Lever API, see here: https://lever.github.io/api-docs/#introduction
Identifying Preliminary Keywords
Before analyzing the job applications, it can be helpful to identify some preliminary keywords that you may want to extract. These keywords can be specific to the position or department you are hiring for. For example, if you are hiring for a marketing position, you may want to extract keywords related to social media, content marketing, and SEO.
Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract the most relevant keywords from the job applications. This will help you quickly identify the most qualified candidates and improve the efficiency of your hiring process.
Using NLP analysis to extract keywords from job applications can also help you identify areas for improvement in your hiring process. For example, if you notice that successful applications have a specific set of keywords, you can adjust your job descriptions and application questions to better target the candidates you are looking for.
NLP analysis and generative AI can simplify the hiring process by identifying the most important keywords in job applications. By using this technology, you can quickly identify the most qualified candidates and improve the efficiency of your hiring process. Whether you are a hiring manager or part of a recruiting team, NLP analysis can help you find the right candidates for your organization.