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

Keyword Identification

Hootsuite

Social media is a treasure trove of customer feedback, but manually going through every post and comment is a daunting task. However, with the help of generative AI, you can quickly and easily extract valuable insights from your Hootsuite social media posts. In this post, we’ll show you how to extract keywords using natural language processing (NLP) analysis and Hootsuite’s SaaS tool.

What is Keyword Extraction?

Keyword extraction is the process of automatically identifying the most important words or phrases in a piece of text. It can be used for various purposes, such as understanding customer sentiment, monitoring brand reputation, and identifying trends.

NLP algorithms are used to analyze the text and identify the most relevant words and phrases. These algorithms can be trained on a labeled dataset of text, which allows them to recognize patterns and features associated with important keywords.

Example Use Cases

Keyword extraction can be used in various use cases for social media, including:

Understanding customer sentiment and feedback
Monitoring brand reputation and mentions
Identifying trends and topics of interest
Improving social media campaigns

Teams that might find these use cases helpful include marketing, customer support, and product.

Accessing Data and Identifying Preliminary Keywords

The first step in extracting keywords from Hootsuite social media posts is to access the data you want to analyze. This can be done by exporting a CSV file of your social media posts from Hootsuite or by using Hootsuite’s API.

Once you have your data, it can be helpful to identify preliminary keywords that you want to extract. This can be done by manually reviewing the posts or by using generative AI tools to automatically identify and suggest keywords.

Hootsuite’s Insights feature provides a sentiment analysis of your social media posts, which can help you understand how customers are feeling about your brand. By combining this with keyword extraction, you can get a deeper understanding of the topics that are driving positive or negative sentiment.

Conclusion

Keyword extraction using generative AI is a powerful tool for analyzing large amounts of social media data. By identifying the most relevant keywords, you can quickly gain insights into customer sentiment, brand reputation, and trends. With Hootsuite’s SaaS tool and NLP analysis, you can easily extract keywords from your Hootsuite social media posts and use them to improve your social media strategy.

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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