How to extract keywords from Instagram Community Channels using generative AI

Keyword Identification

Instagram

If you're looking to improve your social media strategy, extracting keywords from Instagram community channels can provide valuable insights into what content resonates with your audience. In this post, we'll show you how to use generative AI to automatically extract keywords from Instagram community channels and how it can benefit your marketing and content teams.

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 context of Instagram community channels, this means identifying the most commonly used words or hashtags by your audience.

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.

You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

Use cases for extracting keywords from Instagram community channels include:

Identifying popular hashtags and content themes
Monitoring brand and product mentions
Identifying influencers and potential collaborators
Improving engagement and reach
Understanding your audience better

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

Accessing the Data and Identifying Preliminary Keywords

To extract keywords from Instagram community channels, you'll need access to the data. You can use social media monitoring tools like Hootsuite, Sprout Social, or Brandwatch to gather data on your brand's Instagram account or relevant hashtags. Alternatively, you can use Instagram's API to pull data programmatically.

Once you have access to the data, it can be helpful to identify preliminary keywords that you want to extract. This can be done manually or through the use of generative AI tools. These tools can analyze the data and suggest common keywords and themes that may not have been initially considered.

With your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from Instagram community channels. This can save time and effort while providing valuable insights into your audience's interests and behaviors.

Conclusion

Keyword extraction using generative AI is a powerful tool for analyzing and summarizing large amounts of text data from Instagram community channels. By identifying the most important keywords and themes, you can gain valuable insights into your audience's interests and behaviors. This can help inform your marketing and content strategies and ultimately improve engagement and reach on Instagram.

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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