How to analyze sentiment of LinkedIn Groups Community Channels with generative AI

Sentiment Analysis

LinkedIn Groups

As a marketing professional, it's critical to understand the sentiment of your target audience so you can tailor your messaging and improve engagement. LinkedIn Groups provide a unique opportunity to connect with your audience and gather insights, but manually analyzing sentiment across these channels can be time-consuming and inefficient. In this post, we'll show you how to use generative AI for sentiment analysis on LinkedIn Groups Community Channels to save time and gain valuable insights.

What is Sentiment Analysis?

Sentiment analysis is an NLP technique that involves using machine learning algorithms to identify and extract emotions or opinions expressed in text data. The algorithms are trained on labeled datasets of text samples, where each sample is labeled with its corresponding sentiment (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions, and uses these patterns to predict the sentiment of new, unseen text.

Sentiment analysis has many applications, such as customer feedback analysis, social media monitoring, and market research. It's a powerful tool for organizations that want to understand how people feel about their products or services or to track public opinion on different issues. It can help automate tasks and extract valuable insights from large amounts of text data.

Example Use Cases

Some use cases for performing sentiment analysis on LinkedIn Groups Community Channels include:

Understand the sentiment of your target audience towards your brand, products or services
Detect and address negative sentiment before it spreads across your audience
Improve engagement and build a loyal following by understanding what topics your audience is passionate about
Inform content strategy and create tailored messaging that resonates with your audience
Track sentiment over time to assess the impact of marketing campaigns or changes to your brand

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

Accessing Your Data and Confirming Your Sentiment Scale

The first step is to identify the data you want to work with. In this case, we're looking at LinkedIn Groups Community Channels. You can extract the data using the LinkedIn API, export it in CSV format, or query a list of posts from your data warehouse or BI tool.

Next, you need to confirm the sentiment scale you will use for assessing community sentiment. Typically, sentiment is measured on a scale of -1 (most negative) to 1 (most positive). You can also assign sentiment ratings, such as very negative, negative, neutral, positive, and very positive.

Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your LinkedIn Groups Community Channels. This will help you gain valuable insights into your audience sentiment and tailor your marketing efforts accordingly. It will also save you time and resources that would otherwise be spent on manual sentiment analysis.

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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