According to Gartner, 85% of software buyers trust online reviews as much as personal recommendations. Machine learning (ML) can help analyze large volumes of customer reviews across multiple channels to gain insights into customer preferences and improve products and services. Amazon SageMaker Canvas offers ready-to-use AI and custom models for sentiment analysis and text analysis of product reviews without requiring ML expertise. This post provides a step-by-step guide on how to use these models to derive insights from product reviews.
Use AI to Derive Insights from Product Reviews with Amazon SageMaker Canvas
Did you know that 85% of software buyers trust online reviews as much as personal recommendations? Customer feedback and reviews are valuable sources of insights for businesses. However, it can be challenging to process and analyze large volumes of reviews from different channels using traditional methods.
That’s where machine learning (ML) comes in. ML can analyze product reviews, identify patterns, sentiments, and topics discussed. By understanding customer preferences, pain points, and satisfaction levels, businesses can improve their products and services, identify trends, and drive growth.
But implementing ML can be a challenge for companies without ML practitioners or AI developers. That’s where Amazon SageMaker Canvas comes in. It’s a visual, point-and-click service designed for business analysts to use ML without writing code or needing ML expertise.
Ready-to-use Sentiment Analysis Model
With SageMaker Canvas, you can use a ready-to-use sentiment analysis model to determine the sentiments of product reviews. The model can analyze text for sentiments such as positive, negative, mixed, or neutral. You can make predictions interactively or perform batch scoring on bulk datasets.
Custom Text Analysis Model
If you have specific ML use cases for your business, you can train a custom text analysis model with your own data. For example, you can classify product reviews based on product type. You simply provide a dataset with the text and associated categories, and the model learns to categorize the reviews. You can review the model’s performance and retrain if needed before using it for predictions.
How to Use SageMaker Canvas
In this post, we walk you through the steps to use SageMaker Canvas for sentiment analysis and text analysis. We provide sample datasets for you to practice with, and we explain how to import the data, train the models, and make predictions.
Prerequisites:
- Have an AWS account
- Set up SageMaker Canvas
- Download the sample product reviews datasets
Once you have completed the prerequisites, you can follow the step-by-step instructions in the post to perform sentiment analysis and text analysis using the ready-to-use models and custom models. You can also learn how to review the model’s performance and download the prediction results.
Conclusion:
- Amazon SageMaker Canvas allows business analysts to use ML without coding or ML expertise
- Ready-to-use sentiment analysis model for analyzing product reviews
- Custom text analysis model for classifying reviews based on categories
- Step-by-step instructions and sample datasets provided
Elevate your company with AI and stay competitive. Use Amazon SageMaker Canvas to derive insights from product reviews and improve your products and services. Visit itinai.com to learn more about AI solutions and how they can redefine your way of work.
