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

Keyword Identification

Planoly

Social media platforms like Instagram are great for promoting your brand, but it can be difficult to keep track of all the content you're putting out there. Fortunately, there's a way to extract keywords from your Planoly social media posts using generative AI. In this post, we'll show you how to use this technique to identify important themes and trends in your social media content.

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. In the case of social media, this could be identifying the most popular hashtags, topics or themes that are being discussed in your posts.

Keyword extraction can be performed 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. This allows you to quickly identify the most important information and themes from your social media content.

Example Use Cases

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

Identifying popular hashtags and keywords
Monitoring sentiment and engagement
Identifying trends in your content strategy
Comparing your content to that of your competitors
Optimizing your content for social media SEO

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

Accessing Your Planoly Data

In order to extract keywords from your Planoly social media posts, you'll need to access your data. Planoly offers an API that allows you to retrieve your social media posts and related data. You can use this API to retrieve your data in JSON format, which can then be analyzed using generative AI tools.

For more information on the Planoly API see here: https://developers.planoly.com/

Once you have your data, you can begin analyzing it using generative AI tools. These tools can help you identify important keywords and themes in your social media content, allowing you to optimize your content strategy and improve your social media performance.

Identifying Preliminary Keywords

Before you start analyzing your Planoly data, it can be helpful 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, if you're a fashion brand, you might want to identify keywords related to specific clothing items or fashion trends. Once you have these keywords, you can use generative AI to identify which posts are most relevant to these keywords, and analyze the sentiment and engagement of these posts.

Conclusion

Extracting keywords from your Planoly social media posts using generative AI is a powerful tool for improving your social media performance. By identifying important themes and trends in your content, you can optimize your content strategy and improve engagement with your followers. Whether you're a social media manager, marketer, or content creator, implementing keyword extraction techniques can help you achieve your social media goals and grow your brand.

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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