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

Keyword Identification

Later

How to Extract Keywords from Later Social Media Posts Using Generative AI

Social media is a goldmine of customer insights, but manually analyzing every post is impractical. Fortunately, you can use generative AI to automatically extract keywords from Later social media posts. In this post, we’ll show you how to use generative AI to identify the most important information and themes from your Later social media posts.

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 used to extract key information and themes from text, such as search engine optimization (SEO), content analysis, and topic modeling.

Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms, which learn to recognize patterns and features in the text associated with important words or phrases. This can be trained on a labeled dataset of text, making it easier and more efficient to analyze large amounts of text data.

Example Use Cases

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

Identifying popular topics and trends among your audience
Monitoring sentiment and feedback about your brand or products
Optimizing social media campaigns and content
Improving customer engagement and interaction

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

Accessing and Identifying Preliminary Keywords

You first need to identify the data that you want to work with. Here, we are looking at Later social media posts, which can be accessed through the Later API or exported in CSV format.

It can also be helpful (but not necessary) to identify common keywords that you may want to extract from your social media posts. Generative AI tools can be used to both identify and measure 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 product features may provide insights into product improvement opportunities non-obvious to the initial inquiry.

Identifying Keywords with Generative AI

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Later social media posts. Within Later's analytics dashboard, you can use the "Keyword Analysis" feature to see which keywords are most frequently used across your social media posts. This can help you identify popular topics and trends among your audience, monitor sentiment and feedback about your brand or products, optimize social media campaigns and content, and improve customer engagement and interaction.

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.

If you have feedback, please let us know!