How to extract keywords from CoSchedule Social Media Posts using generative AI

Keyword Identification

CoSchedule

Social media is a powerful tool for businesses to connect with their customers and promote their brand. But with so many posts, it can be difficult to keep track of what’s working and what’s not. That’s where keyword extraction comes in. In this post, we’ll show you how to use generative AI to automatically extract keywords from CoSchedule social media posts, so you can get the insights you need to improve your social media strategy.

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 be done 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.

Keyword extraction can be used for search engine optimization (SEO), content analysis, and topic modeling. In the case of CoSchedule social media posts, it can help you identify the most effective keywords and themes for your social media strategy.

Example Use Cases

Use cases for extracting keywords from CoSchedule social media posts include:

Identifying the most effective keywords and themes for your social media strategy
Improving your social media engagement and reach
Analyzing the competition and identifying trends

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

Accessing the Data and Identifying Preliminary Keywords

To access the data, you will need to log in to your CoSchedule account and navigate to the Social Messages section. From there, you can export your data in CSV format, which can then be analyzed using a generative AI tool.

Before you start analyzing your data, it can be helpful to identify preliminary keywords that you may want to extract from your social media posts. These could be related to your brand, product, or industry. Generative AI tools can help you identify these keywords, as well as measure their frequency and suggest additional keywords you may not have been aware of.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract the most effective keywords and themes from your CoSchedule social media posts. This will help you improve your social media engagement and reach, as well as identify trends and analyze the competition.

By using generative AI to extract keywords from your CoSchedule social media posts, you can get the insights you need to improve your social media strategy and achieve your business goals.

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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