How to extract keywords from Podium Online Reviews using generative AI

Keyword Identification

Podium

As a business owner, it is important to identify the strengths and weaknesses of your brand through customer reviews. However, manually extracting keywords from hundreds or thousands of reviews is a tedious and time-consuming task. In this post, we will show you how to use generative AI to automatically extract keywords from Podium online reviews and gain valuable insights into your business.

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. It is a useful tool to extract key information and themes from text data, such as online reviews. Machine learning algorithms can be used to automate the process of keyword extraction by recognizing patterns and features in the text associated with important words or phrases.

Example Use Cases

Use cases for extracting keywords from Podium online reviews include:

Identifying common themes and trends in customer feedback
Improving the quality and consistency of customer service
Tracking the effectiveness of marketing campaigns
Identifying areas for product improvement and innovation

Teams that may find these use cases helpful include: customer service, product development, marketing, and operations.

Accessing Podium Online Reviews and Identifying Preliminary Keywords

The first step is to access the Podium online reviews you wish to analyze. This can be done by logging into your Podium account and exporting the reviews in CSV format. Once you have access to the data, you can start identifying preliminary keywords that you want to extract from the reviews. This can be done manually by identifying common themes and patterns in the reviews, or you can use generative AI tools to help you identify and measure the frequency of keywords.

It is important to note that generative AI tools are not a replacement for human analysis, but rather a tool to help you identify keywords you may not have considered. Additionally, generative AI tools can help you save time and resources by automating the keyword extraction process.

Extracting Keywords using Generative AI

Once you have identified your preliminary keywords, you can use generative AI to automatically extract keywords from your Podium online reviews. There are several tools available that can help you with this task, such as MonkeyLearn, Google Cloud Natural Language API, and Amazon Comprehend.

Generative AI tools can help you identify not only the frequency of keywords but also the sentiment associated with them. This can help you better understand your customers' feedback and identify areas for improvement.

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!