How to extract keywords from Meetup Community Channels using generative AI

Keyword Identification

Meetup

If you're looking to extract valuable insights and information from your Meetup community channels, look no further than keyword extraction using generative AI. In this post, we'll walk you through what keyword extraction is, its use cases, and how to access and analyze your Meetup data.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. It uses machine learning algorithms 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. The result is the ability to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

Use cases for keyword extraction in Meetup community channels include:

Identifying popular topics or trends within your community
Improving event planning and organization
Understanding the sentiment of your community members
Identifying areas for improvement in community engagement
Creating targeted marketing campaigns based on community interests

Teams that might find these use cases helpful include: community management, marketing, data analysis, and event planning.

Accessing and Analyzing Meetup Data

To access your Meetup data, you'll need to use the Meetup API or export your data in CSV format. Once you have your data, you can use generative AI tools to both identify and measure the frequency of keywords, and to suggest additional keywords you may not have been aware to look for. This can help you better understand the interests and sentiment of your community members.

Some preliminary steps to take before analyzing your Meetup data include:

Defining your goals and objectives for the analysis
Identifying relevant keywords or topics to search for
Cleaning your data to remove any irrelevant or duplicate information
Transforming your data into a format that can be analyzed by generative AI tools

With your data prepared, you can use generative AI tools to analyze your Meetup community channels and extract valuable insights and information. This can help you improve your community engagement, better understand the interests and needs of your community members, and create more targeted and effective marketing campaigns.

Conclusion

Keyword extraction using generative AI is a powerful and cost-effective tool for analyzing text data, and can provide valuable insights and information for a variety of teams and use cases. By following the steps outlined in this post, you can access and analyze your Meetup community channels to improve your community engagement, better understand your community members, and create more targeted and effective marketing campaigns.

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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