How to extract keywords from Zoho Recruit Job Applications using generative AI

Keyword Identification

Zoho Recruit

How to Extract Keywords from Zoho Recruit Job Applications Using Generative AI

As a recruiter, you know the importance of quickly identifying the most qualified candidates from a large pool of job applications. But manually reviewing each application can be time-consuming and inefficient. Fortunately, there is a cost-effective solution using generative AI to automatically extract keywords from Zoho Recruit job applications. In this post, we’ll show you how to use keyword extraction to improve your recruitment process.

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, such as job applications. Machines can be trained to recognize patterns and features in the text that are associated with important words or phrases, and can automatically extract them.

Keyword extraction can help you quickly identify the most qualified candidates from a large pool of job applications. It can also help you identify common skills and qualifications that are important for a particular job.

Example Use Cases

Use cases for keyword extraction in Zoho Recruit job applications include:

Quickly identifying the most qualified candidates
Identifying common skills and qualifications for a particular job
Improving the efficiency of your recruitment process
Identifying areas for training and improvement for your recruitment team

Teams that might find these use cases helpful include: recruitment, HR, and hiring managers.

Accessing the Data and Identifying Preliminary Keywords

The first step is to identify the data that you want to work with. Here, we are looking at Zoho Recruit job applications. You can extract this data using the Zoho Recruit API, export it in CSV format, or query a list of job applications from your data warehouse or BI tool.

For more information on the Zoho Recruit API see here: https://www.zoho.com/recruit/api/

Next, it can be helpful to identify common keywords that you may want to extract from your job applications. Generative AI tools can be used to both identify and measure the frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example, you might find that recurring skills or qualifications are necessary for the job but not immediately obvious.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Zoho Recruit job applications. This will help you quickly identify the most qualified candidates and improve the efficiency of your recruitment process.

Conclusion

Keyword extraction using generative AI is a cost-effective and efficient way to improve your recruitment process by quickly identifying the most qualified candidates and important skills and qualifications for a particular job. By using this technique, you can save time and resources while improving the quality of your recruitment process.

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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