How to extract keywords from Lever Job Applications using generative AI

Keyword Identification

Lever

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.

Conclusion

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.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

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