How to classify Zoho Recruit Job Applications with generative AI

Text Classification

Zoho Recruit

How to Classify Zoho Recruit Job Applications with Generative AI

If you are a hiring manager or recruiter, you know how time-consuming and challenging it can be to manually sort through job applications. With the help of generative AI, you can automate this process and quickly categorize job applications based on their qualifications and experience. In this post, we will show you how to use text classification with Zoho Recruit to make the hiring process more efficient.

What is Text Classification?

Text classification is a type of natural language processing (NLP) that uses machine learning algorithms to automatically classify text into predefined categories or labels. It involves teaching an algorithm to recognize patterns and features in the text that can be used to assign a category or label to a new piece of text. Text classification has become an essential tool for many industries that rely on large amounts of text data.

Example Use Cases

By using text classification with Zoho Recruit, you can:

Automatically categorize job applications based on qualifications and experience
Identify and filter out irrelevant applications
Quickly identify top candidates for specific job roles
Streamline the hiring process and reduce manual labor

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

Accessing Your Data and Identifying Categories

The first step in implementing text classification with Zoho Recruit is to identify the data you want to classify. This could include resumes, cover letters, and applications. Once you have access to this data, you can begin to identify the categories that you want to use for classification.

Common examples of job application categories include:

Education and qualifications
Work experience
Skills and abilities
Personal information
References

Once you have your data and categories, you can begin to train your generative AI algorithm to automatically classify job applications based on the information provided.

Conclusion

Text classification with generative AI can be a powerful tool for streamlining the hiring process and improving the efficiency of your recruitment team. By automating the categorization of job applications, you can save time and reduce the risk of human error. Whether you are a small business or a large enterprise, using text classification with Zoho Recruit can help you find the right candidates for the job.

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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