How to classify Reddit Social Media Posts with generative AI

Text Classification

As social media continues to grow at an exponential rate, it has become increasingly important for businesses to monitor and analyze what their customers are saying online. Reddit, a popular social media platform, is an excellent source of customer feedback, comments, and suggestions. In this post, we will show you how to use generative AI to classify Reddit social media posts and gain valuable insights into your customers' opinions.

What is Text Classification?

Text classification is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign one or more predefined categories or labels to a given piece of text. The algorithms typically learn from a training 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 spam detection in emails to sentiment analysis in social media posts and reviews. It has become an essential tool for many industries that rely on large amounts of text data, helping to automate tasks and extract valuable insights from the data.

Example Use Cases

Use cases for classifying Reddit social media posts include:

Identify customer sentiment towards your brand or product
Monitor competitor mentions and reactions
Track trending topics and discussions in your industry
Identify and respond to customer complaints or issues
Measure the success of marketing campaigns

Teams that might find these use cases helpful include: marketing, customer support, product, and operations.

Finding your Input Data and Categories

You first need to identify the data that you want to work with. Here, we are looking at Reddit social media posts. You can extract this data using the Reddit API, export it in CSV format, query a list of posts from your data warehouse or BI tool, or copy and paste with an example post.

For more information on the Reddit API see here: https://www.reddit.com/dev/api/

Next, you need to find or create your list of categories for classifying the posts. This might include sentiment categories such as positive, negative, or neutral, or topic categories such as product feedback, customer service, or marketing campaign.

Once you have your data and categories, you can use generative AI to automatically classify your Reddit social media posts. This will help you to extract valuable insights from the data and improve your overall customer experience.

Step-by-Step Instructions

Identify the data you want to classify by extracting Reddit social media posts.
Create a list of categories for classification. This might include sentiment categories or topic categories.
Select a generative AI tool for text classification, such as Google Cloud Natural Language or Amazon Comprehend.
Train the generative AI tool on your labeled text data.
Test the generative AI tool on new, unseen text data to ensure accuracy.
Once the generative AI tool is trained and tested, use it to automatically classify your Reddit social media posts.

By following these steps, you can quickly and easily classify your Reddit social media posts and gain valuable insights into your customers' opinions and feedback.

Conclusion

Text classification is a powerful tool that can help businesses extract valuable insights from large amounts of text data. By using generative AI to classify Reddit social media posts, businesses can gain a better understanding of their customers' opinions and feedback, monitor competitor mentions and reactions, and track trending topics and discussions in their industry. With the right tools and techniques, businesses can use text classification to improve their overall customer experience and drive growth.

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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