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

Keyword Identification

Agorapulse

Social media is a valuable tool for any business, but it can be overwhelming to sift through all the data. That's where keyword extraction comes in. By using generative AI, you can quickly and easily identify the most important keywords from your Agorapulse social media posts. In this post, we’ll show you how to do just that.

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. You can use it to extract key information and themes from text has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.

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 of. 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 Agorapulse social media posts include:

Identifying common themes and topics
Monitoring brand sentiment
Tracking competitor activity
Optimizing social media campaigns
Improving customer engagement and satisfaction

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

Accessing and Analyzing Your Data

First, you need to export your Agorapulse social media post data. You can export your data from Agorapulse in CSV format, query a list of posts from your data warehouse or BI tool, or copy and paste with an example post.

Next, you can use generative AI to automatically extract keywords from your Agorapulse social media posts. This will help you quickly identify the most important themes and topics. You can then use this information to optimize your social media campaigns, monitor brand sentiment, and improve customer engagement and satisfaction.

Some additional tips for analyzing your data include:

Use filters to narrow your data down to specific time frames or social media platforms
Identify patterns in the data, such as spikes in engagement or changes in sentiment
Track changes over time to see the effectiveness of your social media campaigns

Conclusion

By using generative AI to extract keywords from your Agorapulse social media posts, you can quickly and easily identify the most important themes and topics. This can help you optimize your social media campaigns, monitor brand sentiment, and improve customer engagement and satisfaction. Give it a try and see how it can benefit 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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