How to classify Podium Online Reviews with generative AI

Text Classification

Podium

As a business, it is essential to monitor your online reputation and respond to customer reviews. However, it can be time-consuming to read through each review and determine its sentiment and topic manually. In this post, we'll show you how to use generative AI to automatically classify Podium online reviews to save you time and help you respond effectively.

What is Text Classification?

Text classification is an NLP technique that uses machine learning algorithms to assign predefined categories or labels to a given piece of text. It involves training a model on labeled data and using that knowledge to classify new, unseen text. The model learns to identify patterns and features in the text that correspond to specific categories, such as sentiment or topic.

Text classification is widely used in many industries, including e-commerce, marketing, and customer support. It can help businesses automate tasks, extract valuable insights from text data, and improve customer experiences.

Example Use Cases

Use cases for classifying Podium online reviews include:

Automatically classify reviews by sentiment (positive, negative, neutral)
Automatically classify reviews by topic (service, product, delivery, etc.)
Identify and flag reviews that require urgent attention
Auto-tag reviews based on specific keywords or phrases
Aggregate and analyze review data to identify trends and patterns

The teams that might find these use cases helpful include marketing, customer support, and operations.

Finding your input data and categories

To classify Podium online reviews, you first need to identify the data that you want to work with. Podium has an API that allows you to extract review data programmatically. Alternatively, you can export the reviews in CSV format or copy and paste them into a text file.

Next, you need to determine the categories that you want to classify the reviews into. For example, you may want to classify reviews based on their sentiment (positive, negative, or neutral) or their topic (service, product, delivery, etc.). You can create your own categories or use pre-existing taxonomies.

Here are some common categories for classifying reviews:

Service
Product
Delivery
Price
Location
Customer support
Marketing
Overall experience

Once you have your data and categories, you can use generative AI to automatically classify your Podium online reviews. This will help you save time, respond effectively to reviews, and extract valuable insights from your review data.

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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