How to extract keywords from Bazaarvoice Online Reviews using generative AI

Keyword Identification

Bazaarvoice

Online reviews are essential for businesses to understand their customers' opinions and make informed decisions. However, analyzing large amounts of text data can be overwhelming and time-consuming, especially for teams with limited resources. That's where keyword extraction using generative AI comes in. In this post, we'll explain how you can use this technique to extract valuable insights from Bazaarvoice online reviews.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique used to identify the most important or relevant words or phrases in a piece of text. It helps to extract key information and themes from text for various applications, such as search engine optimization (SEO), content analysis, and topic modeling.

Automated keyword extraction involves using machine learning algorithms to recognize patterns and features in the text associated with important words or phrases. These algorithms can be trained on a labeled dataset of text to learn how to identify relevant keywords and phrases.

Keyword extraction is particularly useful for analyzing and summarizing large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

Some example use cases for keyword extraction from Bazaarvoice online reviews include:

Identifying popular products and features: Keyword extraction can help businesses identify the most popular products and features among their customers. This can help product teams prioritize new feature development and marketing teams create targeted campaigns.
Tracking customer sentiment: Keyword extraction can help businesses track customer sentiment by identifying commonly used positive or negative keywords. This can help customer support teams respond to negative reviews quickly and improve the overall customer experience.
Competitive analysis: Keyword extraction can help businesses compare their products and services to their competitors by identifying the most commonly used keywords and phrases in their reviews.

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

Accessing and Analyzing Bazaarvoice Online Reviews

To access Bazaarvoice online reviews, you can use the Bazaarvoice API or export the data in CSV format. Once you have the data, you can use a generative AI tool to identify and extract relevant keywords.

Before analyzing the data, it can be helpful to identify preliminary keywords. These can be keywords relevant to your business or industry, or commonly used keywords in your reviews. Generative AI tools can assist in both identifying and measuring the frequency of keywords, as well as suggesting additional keywords you may not have considered.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Bazaarvoice online reviews. This will help you identify common themes and important information in your reviews quickly and efficiently. You can use this information to improve your products, services, and customer experience.

Conclusion

Keyword extraction using generative AI is a powerful technique that can help businesses extract valuable insights from large amounts of text data. By applying this technique to Bazaarvoice online reviews, businesses can quickly and efficiently identify important information and themes in their customer feedback. This can help teams make informed decisions and improve the overall customer experience.

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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