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

Keyword Identification

LinkedIn Groups

As a data analyst, you know how valuable insights can be for a business. One of the best sources of insights is the conversations happening in LinkedIn Groups Community Channels. However, manually combing through each conversation is impractical and inefficient. Fortunately, with generative AI, you can automatically extract keywords from these conversations to identify important insights. In this post, we’ll show you how.

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. The process involves analyzing a large set of text data and using algorithms to identify and extract the most important or relevant keywords. You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Keyword extraction can be performed manually, but it can also be automated using 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.

Example Use Cases

Use cases for extracting keywords from LinkedIn Groups Community Channels include:

Identifying trends and topics that are popular in your industry
Uncovering customer pain points that you can address in your product or service
Identifying competitors and how they are positioning themselves in the market
Identifying potential partners or collaborators in your industry

Teams that might find these use cases helpful include: marketing, product, business development, and strategy.

Accessing Your Data and Identifying Preliminary Keywords

You first need to identify the data that you want to work with. In this case, you will need to access the LinkedIn Groups Community Channels that you want to analyze. You can do this by joining the relevant groups and accessing the conversations. Once you have access to the data, you can export it as a CSV file or copy and paste into a text editor.

Before you start extracting keywords, it can be helpful (but not necessary) to identify common keywords that you may want to extract from the conversations. Generative AI tools can be used to both identify and measure the frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example, you might find that recurring customer inquiries around a specific topic may provide insights into product improvement opportunities non-obvious to the initial inquiry.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from the conversations. There are several SaaS tools available that can help you with this process, such as Aylien, or Google Cloud Natural Language API, and AirOps.

Extracting keywords from LinkedIn Groups Community Channels can provide valuable insights for your business. By using generative AI, you can automate the process and quickly identify the most important information and themes. With the right tools and techniques, you can gain a competitive advantage and make data-driven decisions that will help you grow your business.

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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