Amazon uses sentiment analysis to comprehend the underlying emotion of the people behind the text. These include emails, blog posts, reviews, comments, and others. The analysis yields insights that sellers on amazon can use to enhance their products and services.
Sentiment analysis can revolutionize the way sellers evaluate their products. Which means more business for the seller and happier customers. Are you thinking about implementing sentiment analysis in your business? Well, let's learn how Amazon Web Services lets you do that.
The Basics Of Sentiment Analysis
Before implementing sentiment analysis in your business, it's helpful to develop a bit of an idea on the basics. Essentially, sentiment analysis is the extracting the genuine emotion of people from a single or collection of text using Artificial Intelligence.
For instance, even midsize businesses generate large volumes of text data. These exist in the form of Facebook comments, review posts, emails, messages to customer services, and others.
Natural language processing tools implement Machine learning technologies to scan these piles of text. Consequently, the tools come up with reports that accurately define the customers' attitudes (positives, negatives, and scores) towards specific aspects of the company's service. This helps owners get past personal biases about their businesses.
How Does Sentiment Analysis Work?
Software with Natural Language Processing technologies carries out the task of Sentiment Analysis. This software help devices understand how humans comprehend texts. The analysis tool, for instance, Amazon Comprehend accesses these texts from a data lake like Amazon S3.
The first phase is pre-processing where the keyword is identified. This helps sentiment analysis acquire the gist of the text. After that, tokenization begins. In this phase, the text is reduced to several tokens.
Then the process rids the text of all kinds of ornaments. For instance, words reduce to their root forms, and stop words (with, at, of, etc.) disappear from the text. This helps the NLP evaluate the keyword better and finally assign a sentiment score. Usually, a score of 10 means customer satisfaction. Whereas, 0 means dissatisfaction.
The final data is processed in a data warehouse. For instance, Amazon has Amazon Redshift. Here, the extracted data goes further processing to yield the best possible action to enhance customer satisfaction. You can easily sign up for an AWS account and start using amazon sentiment analysis.
The Importance Of Sentiment Analysis
You might wonder why you need to go through all these troubles with sentiment analysis and AI. Why not just binge-read through your reviews, comments, and blog posts regarding your products? Well, first of all, businesses today generate text data faster than you can read them. So, it's humanly impossible to sort through that pile.
Secondly, Natural Language Processing mimics the reading habits of human beings. However, that doesn't mean it has the same shortcomings as the human reader.
When you read the texts regarding your company products, chances are great that your personal biases and opinions will be at the back of your mind. Therefore, you will subconsciously avoid the negative tone and disappointment of your customer.
Meanwhile, artificial intelligence is free from such bindings. As a result, they will scan, analyze and produce results purely based on facts. Therefore, their reports will show you the true situation of your business and help you take the best action going forward.
Conclusion
AWS sentiment analysis is a great tool for businesses to better understand the mind of their customer. However, you can also try other artificial intelligence-based sentiment analysis to explore more possibilities. For instance, Shulex’s VOC SaaS product that can help businesses get global and deeper insights about their services. Start your free trial today.
