How to classify Pinterest Social Media Posts with generative AI

Text Classification

If you're managing a Pinterest account for your business, you know how challenging it can be to keep up with the volume of posts and comments. Manually categorizing posts by topic or sentiment can be time-consuming and lead to errors. In this post, we'll show you how to use generative AI to automatically classify Pinterest social media posts, saving you time and improving your customer engagement.

What is Text Classification?

Text classification is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign predefined categories or labels to a given piece of text. The algorithms learn from a set of labeled text data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.

Text classification is used in a wide range of applications, from sentiment analysis to content categorization. It's an essential tool for businesses that rely on social media data to track customer engagement and improve their social media strategy.

Example Use Cases

Use cases for classifying Pinterest social media posts include:

Identify popular content to inform content creation
Track sentiment towards your brand, products, or services
Identify customer needs and pain points
Track competitor activity
Improve customer engagement and response times

Teams that might find these use cases helpful include: social media managers, marketing, customer support, and product development.

Finding your input data and categories

You first need to identify the data that you want to work with. Here, we are looking at Pinterest posts. You can extract this data using the Pinterest API or export it in CSV format from Pinterest Analytics.

For more information on the Pinterest API, see here: https://developers.pinterest.com/docs/getting-started/introduction/

Next, you need to find or create your list of categories for classifying the posts. This might include categories based on topic, sentiment, or engagement level.

Common examples of Pinterest post categories include:

DIY and craft projects
Recipes and food ideas
Fashion and beauty
Home decor and design
Travel and vacations
Weddings and special occasions
Product reviews and recommendations
Company news and updates

Once you have your data and categories, you can use generative AI to automatically classify your Pinterest social media posts. This will help you to save time, improve your response times to customer comments, and identify trends in customer engagement.

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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