How to extract keywords from Feefo Online Reviews using generative AI

Keyword Identification

Feefo

How to Extract Keywords from Feefo Online Reviews using Generative AI

As a business, you want to understand your customers’ feedback and sentiments about your products or services. One of the most valuable sources of this feedback is online reviews. However, analyzing and extracting insights from thousands of reviews manually is impossible. This is where Keyword Extraction using generative AI comes in. In this post, we’ll show you how to use generative AI to automatically extract keywords from Feefo online reviews.

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 online reviews, this means identifying the words or phrases that customers use most frequently to describe their experience with your product or service.

Automated keyword extraction tools, like those that use generative AI, can analyze thousands of reviews quickly and accurately. They can identify the most commonly used words and phrases and cluster them into themes, giving you a clear understanding of what your customers are saying about your business.

Example Use Cases

Use cases for extracting keywords from Feefo online reviews include:

Identifying common pain points or issues that customers face with your product or service
Measuring customer satisfaction and sentiment towards your business
Gaining insight into customer preferences and behavior
Identifying areas for product or service improvement
Developing targeted marketing campaigns based on customer feedback and preferences

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

Accessing your Data and Identifying Preliminary Keywords

To extract keywords from Feefo online reviews, you will need access to your Feefo account and the ability to export the reviews in a suitable format. You can export the reviews in CSV format from the Feefo dashboard.

You can also use generative AI to help you identify preliminary keywords to extract from the reviews. These tools can analyze your reviews and suggest keywords and themes based on frequency and relevance. For example, if you sell shoes, these tools may suggest keywords such as “comfort,” “fit,” “style,” and “quality.”

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract the most relevant and commonly used keywords from your Feefo reviews. This will help you understand your customers’ feedback and sentiment towards your business more quickly and accurately.

Conclusion

Keyword Extraction using generative AI is a powerful tool that can help businesses analyze customer feedback quickly and accurately. By identifying the most commonly used words and phrases in online reviews, businesses can gain valuable insights into customer preferences, pain points, and sentiment towards their product or service. By following the steps outlined in this post, you can start extracting keywords from your Feefo online reviews and gain a better understanding of your customers' needs.

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.

If you have feedback, please let us know!