How to classify LinkedIn Social Media Posts with generative AI

Text Classification

Social media platforms like LinkedIn generate massive amounts of data that can be overwhelming for human analysis. To help businesses extract insights from this data, generative AI can be used to automatically classify social media posts in real-time. In this article, we’ll explain how text classification works and provide examples of how businesses can benefit from using NLP analysis to classify LinkedIn social media posts.

What is Text Classification?

Text classification, also known as text categorization, is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign one or more predefined categories or labels to a given piece of text. The algorithms learn from a training set of labeled text data, and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.

In the case of LinkedIn social media posts, text classification can be used to automatically assign categories to posts based on their content. This can help businesses to analyze social media data more efficiently and extract valuable insights from the data.

Example Use Cases

Use cases for classifying LinkedIn social media posts include:

Identifying trending topics in your industry
Automatically categorizing posts by topic or theme
Identifying influencers and thought leaders in your industry
Monitoring brand sentiment on social media
Identifying potential job candidates

Teams that might find these use cases helpful include: marketing, social media, human resources, and business intelligence.

Finding your input data and categories

You first need to identify the data that you want to work with. In this case, we are looking at LinkedIn social media posts. You can extract this data using the LinkedIn API, export it in CSV format, query a list of posts from your data warehouse or BI tool, or copy and paste with an example post.

For more information on the LinkedIn API, see here: https://developer.linkedin.com/docs/v2

Next, you need to find or create your list of categories for classifying the posts. This might include post topics or themes, sentiment levels, or other relevant metrics.

Common examples of LinkedIn post categories include:

Industry news and trends
Career development and job search advice
Thought leadership and insights
Product or service announcements
Company culture and values
Industry events and conferences
Training and education resources
Sales and marketing promotions

Once you have your data and categories, you can use generative AI to automatically classify your LinkedIn social media posts. This will help you to analyze social media data more efficiently and extract valuable insights from the data.

Conclusion

Text classification using generative AI is a powerful tool for analyzing social media data from platforms like LinkedIn. By automatically categorizing social media posts, businesses can extract valuable insights more efficiently and make data-driven decisions. To get started with text classification, you need to identify your input data and categories, and choose a generative AI tool that fits your needs.

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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