How to classify YouTube Social Media Posts with generative AI

Text Classification

YouTube

How to Classify YouTube Social Media Posts with Generative AI

As a content creator, it's important to monitor and engage with your audience on social media platforms like YouTube. However, manually sorting through comments and messages can be a time-consuming and tedious task. In this post, we'll show you how to use generative AI to automatically classify YouTube social media posts, making it easier to identify and respond to your audience's needs.

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. In the case of YouTube social media posts, the algorithms can learn to classify comments and messages based on their intent, sentiment, or topic. This can help content creators to quickly identify and respond to their audience's needs, improving engagement and satisfaction.

Example Use Cases

Use cases for classifying YouTube social media posts include:

Identifying and addressing negative comments or feedback
Automatically categorizing comments by topic or content
Identifying and responding to frequently asked questions
Identifying and responding to comments from influencers or collaborators
Detecting and preventing spam or inappropriate comments

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

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 YouTube comments and messages. You can extract this data using the YouTube API, export it in CSV format, or copy and paste with an example comment or message.

For more information on the YouTube API, see here: https://developers.google.com/youtube

Next, you need to find or create your list of categories for classifying the comments and messages. This might include topics or themes related to your content, sentiment or emotion, or intent.

Common examples of categories for YouTube social media posts include:

Positive feedback or comments
Negative feedback or comments
Questions and inquiries
Collaboration or partnership opportunities
Spam or inappropriate comments

Once you have your data and categories, you can use generative AI to automatically classify your YouTube social media posts. This will help you to quickly identify and respond to your audience's needs, improving engagement and satisfaction.

By using generative AI to classify YouTube social media posts, content creators can save time and improve engagement with their audience. Whether you're looking to address negative feedback, respond to frequently asked questions, or detect and prevent spam, text classification can help you to quickly identify and respond to your audience's needs.

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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