How to extract keywords from Facebook Groups Community Channels using generative AI

Keyword Identification

Facebook Groups

As a community manager, you know that your Facebook Groups Community Channel is a treasure trove of valuable insights. However, with thousands of messages exchanged every day, it can be overwhelming and time-consuming to sift through each one manually. Luckily, there is an efficient way to extract important insights from your Facebook Groups Community Channel using generative AI. In this post, we’ll show you how to automatically extract keywords from your Facebook Groups Community Channel, and how it can benefit your team.

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 can help you uncover key information and themes from your Facebook Groups Community Channel conversations, such as frequently asked questions, customer pain points, and commonly used phrases or terminology. Keyword extraction can be performed manually, but it is much more efficient and accurate to use machine learning algorithms to automate the process.

Example Use Cases

There are several use cases for extracting keywords from your Facebook Groups Community Channel:

Identifying popular topics and discussions
Understanding customer pain points and needs
Improving community engagement and participation
Optimizing content creation and marketing strategy
Identifying potential brand ambassadors or influencers

Teams that will benefit from these use cases include community management, social media, marketing, and product development.

Accessing your Facebook Groups Community Channel Data and Identifying Preliminary Keywords

The first step in keyword extraction is to access your Facebook Groups Community Channel data. You can do this by exporting your conversations to a CSV file or using a third-party tool that integrates with Facebook API. Once you have your data, you can identify preliminary keywords that you would like to extract. This can be done using manual analysis or by using generative AI tools that can help identify and suggest additional keywords that you may not have considered.

It is important to note that generative AI tools can only provide initial suggestions, and it is up to you to review and validate the keywords extracted. This process will require some knowledge of your brand, niche, and audience, and may require several iterations to refine your keyword list.

Extracting Keywords using Generative AI

There are several generative AI tools that can be used to extract keywords from your Facebook Groups Community Channel. Some popular ones include IBM Watson, Google Cloud Natural Language, and Amazon Comprehend. These tools use machine learning algorithms to analyze your text data and extract relevant keywords based on their frequency, context, and relevance to your topic. You can then use these keywords to create insights, reports, or content strategies that are tailored to your audience needs and preferences.

By leveraging the power of AI, you can save time and resources, while also gaining valuable insights that can help you improve your community engagement and brand reputation. Give it a try and see how it can benefit your team!

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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