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

Keyword Identification

Loomly

Social media is a valuable tool for businesses to connect with their customers and build brand awareness. However, analyzing the large amounts of data generated from social media can be overwhelming. In this post, we’ll show you how to use generative AI to automatically extract keywords from Loomly social media posts, making it easier to identify important insights and themes.

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.

With the help of generative AI, keyword extraction can be automated. Machine learning algorithms can learn to recognize patterns and features in the text that are associated with important words or phrases. This can save businesses valuable time and resources by quickly identifying important insights and themes in large amounts of data.

Example Use Cases

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

Identifying popular topics and trends among followers
Measuring the success of marketing campaigns
Improving social media engagement and reach
Identifying areas for product improvement based on customer feedback
Creating personalized social media content for specific target audiences

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

Accessing your Loomly social media post data and identifying preliminary keywords

To extract keywords from your Loomly social media posts, you will need to export your post data in CSV format. You can do this by navigating to the “Analytics” section of your Loomly account and selecting “Export Data”. Once you have your data, you can use generative AI tools to identify and measure the frequency of keywords. Generative AI tools also help suggest additional keywords you may not have been aware to look for.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Loomly social media posts. This will help you quickly identify important insights and themes, and improve the efficiency of your social media management.

With these tools and techniques at your disposal, you can gain valuable insights from your Loomly social media posts without spending countless hours analyzing data manually. This can help you create more effective social media strategies and improve customer engagement.

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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