How to extract keywords from YouTube Community Channels using generative AI

Keyword Identification

YouTube

How to Extract Keywords from YouTube Community Channels using Generative AI

If you’re in the business of creating content on YouTube, you know how important it is to engage with your audience. One of the best ways to do that is through the YouTube Community tab, which allows you to post updates, polls, and other content to your subscribers. However, with so much content being posted every day, it can be hard to keep track of what your audience is saying. That’s where keyword extraction using generative AI comes in. This technique allows you to quickly and easily identify the most important themes and topics being discussed on your Community tab.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the words and phrases that are most important or relevant in a piece of text. This can be done manually, but it’s much more efficient to use machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text.

Keyword extraction has many applications, such as search engine optimization (SEO), content analysis, and topic modeling. In the case of YouTube Community channels, it can be used to analyze and summarize the comments and discussions being posted by your subscribers.

Example Use Cases

Here are a few examples of how keyword extraction using generative AI can be valuable for YouTube Community channels:

Identifying the most popular topics being discussed by your subscribers
Identifying potential issues or areas for improvement with your content
Tracking the sentiment of your audience over time
Identifying influencers or other channels that your audience is interested in

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

Accessing and Analyzing Your YouTube Community Data

To analyze your YouTube Community data, you will need to use a generative AI tool that is specifically designed for NLP analysis.

Once you have access to your YouTube Community data, you can use these tools to automatically extract the most important keywords and phrases being discussed by your subscribers. You can then use this information to identify the most popular topics, track sentiment over time, and make data-driven decisions about your content and engagement strategies.

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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