How to classify Yelp Social Media Posts with generative AI

Text Classification

Yelp

How to Classify Yelp Social Media Posts with Generative AI

Are you struggling to keep up with the massive amounts of user-generated content on your Yelp page? Do you want to ensure that you are providing top-notch customer service by addressing concerns and complaints quickly? Look no further than generative AI for text classification. In this post, we'll walk you through the basics of text classification and how to use it to automatically categorize your Yelp social media posts.

What is Text Classification?

Text classification is a process of categorizing text into predefined categories based on its content. It is a subset of natural language processing and involves using machine learning algorithms to automatically assign one or more predefined categories or labels to a given piece of text. The algorithms learn from a training set of labeled text data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.

Text classification is widely used in a variety of industries and applications, including spam detection, sentiment analysis, and content recommendation. It can help automate tasks and extract valuable insights from large amounts of text data.

Example Use Cases

Use cases for classifying Yelp social media posts include:

Automatically identify and classify negative reviews or complaints
Automatically prioritize urgent posts that require immediate attention
Identify and classify spam or irrelevant posts
Identify and track trends in customer feedback

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

Finding your Input Data and Categories

You first need to identify the data that you want to work with. In this case, we are looking at Yelp social media posts. You can extract this data using the Yelp Fusion API, export it in CSV format, or use a web scraping tool to extract the data from the Yelp website.

For more information on the Yelp Fusion API see here: https://www.yelp.com/developers/documentation/v3/authentication

Next, you need to find or create your list of categories for classifying the posts. This might include sentiment categories (positive, negative, neutral), urgency levels (low, medium, high), or any other categories that are relevant to your business.

For example, if you are a restaurant, you might want to classify posts into categories such as:

Food quality
Service quality
Ambience
Value for money
Location
Cleanliness
Wait time
Special dietary needs

Once you have your data and categories, you can use generative AI to automatically classify your Yelp social media posts. This will help you to quickly identify and address customer concerns, track trends in customer feedback, and improve your overall customer service.

Conclusion

Text classification with generative AI is a powerful tool that can help businesses of all sizes to automate tasks and extract valuable insights from large amounts of text data. By using this technique to automatically classify your Yelp social media posts, you can save time and improve your overall customer service. Try it out today!

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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