How to classify Discord Community Channels with generative AI

Text Classification

Discord

Discord is a powerful communication platform that enables communities to connect and collaborate. However, as communities grow, it can become challenging to manage the channels and ensure that they are organized effectively. In this post, we will explore how to use generative AI to automatically classify Discord community channels, making it easier to manage your community.

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 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 various applications, including spam detection, sentiment analysis, and topic classification. It has become an essential tool in many industries that rely on large amounts of text data.

Example Use Cases

Use cases for classifying Discord community channels include:

Automatically classify channels by topic
Automatically classify channels by language
Identify and classify spam channels
Automatically prioritize important channels
Organize channels by purpose or function

Teams that might find these use cases helpful include community managers, moderators, and administrators.

Finding Your Input Data and Categories

You first need to identify the data that you want to work with. In this case, we are looking at Discord community channels. You can extract this data using the Discord API, export it in CSV format, or copy and paste the channel names into a text file.

Next, you need to find or create your list of categories for classifying the channels. This might include topics, languages, or purposes.

Common examples of community channel topics include:

Gaming
Music
Art
Technology
Sports
Politics
News
Education
Entertainment
Science

Once you have your data and categories, you can use generative AI to automatically classify your Discord community channels. This will help you to organize your community effectively and ensure that channels are grouped together based on their purpose.

For more information on the Discord API, see here: https://discord.com/developers/docs/intro

Conclusion

Using generative AI to classify Discord community channels can be a powerful tool for community managers, moderators, and administrators. It can help you to organize your community effectively and ensure that channels are grouped together based on their purpose. By following these instructions, you can get started with text classification and make managing your community easier and more efficient.

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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