How to extract keywords from Capterra Online Reviews using generative AI

Keyword Identification

Capterra

Online reviews are a goldmine of valuable insights for businesses looking to improve their products and services. However, manually sifting through hundreds or thousands of reviews can be a daunting task. Fortunately, there is a way to automate the process and extract valuable keywords using generative AI. In this post, we'll show you how.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying important words or phrases in a piece of text. These keywords can then be used to summarize the text and identify key themes. Keyword extraction can be done manually but is often automated using machine learning algorithms that are trained on a labeled dataset of text.

Example Use Cases

Use cases for extracting keywords from Capterra reviews include:

Identifying common features or issues with a product
Comparing products against each other
Identifying areas for improvement or development
Tracking customer sentiment over time
Improving SEO by identifying popular search terms

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

Accessing the Data and Identifying Preliminary Keywords

Before you can extract keywords from Capterra reviews, you need to access the data. Capterra provides an API that allows you to retrieve reviews for a specific product or category. You can also export reviews in CSV format or scrape them using web scraping tools.

Once you have the data, it can be helpful to identify common keywords that you want to extract. These can be words or phrases that are frequently mentioned in the reviews and are relevant to your analysis. Generative AI tools can also be used to suggest additional keywords that you may not have considered.

Once you have your data and preliminary keywords, you can use generative AI to automatically extract keywords from the reviews. These keywords can be used to identify common themes, sentiment, and popular search terms.

Conclusion

Keyword extraction using generative AI is a powerful tool for businesses looking to analyze large amounts of text data. With the ability to automatically extract keywords from Capterra reviews, businesses can quickly identify key themes and areas for improvement. By using this technique, businesses can improve their products and services, track customer sentiment, and improve their SEO.

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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