What is Sentiment Analysis and Text Analytics? (+ Quick Video)

Text analytics and sentiment analysis often come hand-in-hand when monitoring customer feedback. 

Both are essential to a successful program for customer experience management because they allow you to derive meaning from the data you collect about your customers. However, in practice, they’re actually quite different. 

In this article, we’ll explain their major differences and how they can be used to analyze your data. 

Textual data, such as emails, social media interactions, and chats, can be challenging to analyze because of the lack of structure in the data. It’s time-consuming and expensive to sort through text by hand, especially if you need to recruit additional people to help with the task.

It is possible to automatically extract useful information from unstructured text data using text analytics (TA), a machine learning technique. Companies use text analytics tools to quickly absorb web data and documents and turn them into useful insights.

For instance, the text analytics of tens of thousands of emails can extract specific information such as keywords, company names, or corporate data or categorize survey replies according to sentiment and topic. 

According to EDUCBA, text mining is basically cleaning up data to be available for text analytics. Oftentimes, however, text mining and text analytics are terms used interchangeably.

Knowledge-based organizations frequently use text mining to identify new information or answer specific research queries.

Text mining uncovers information that would otherwise be buried beneath mountains of unstructured text. It is then possible to evaluate and show this data in various ways, such as clustered HTML tables, mind maps, and other visual representations, depending on how it is extracted. 

Natural Language Processing (NLP) is a critical component of text mining since it allows several text processing methods.

Text mining produces structured data that may be connected to databases, data warehouses, or business intelligence dashboards for descriptive, prescriptive, and predictive analytics.

To learn more about text analytics:

• Check out our in-depth Text Analysis 101 guide 
• Also concrete text analysis examples for social media sentiment, workforce performance, competitor intelligence, and more
• Or watch this quick video (made of two parts) by Wonderflow’s CEO on the power of text analytics:

Sentiment analysis is the process of determining whether or not a piece of writing—such as a consumer product review—has a positive or negative tone. Many business organizations increasingly rely on sentiment analysis to assess their brand’s reputation and learn about their customers.

A text’s sentiment can be assessed by sentiment analysis, which examines the text’s polarity (positive, negative, neutral), as well as its emotion (angry, glad, sad, etc.), urgency (urgent, not urgent), and even its intent (interested v. not interested).

You can establish and customize your categories when analyzing client comments and inquiries to fit your sentiment analysis needs.

By automatically evaluating consumer feedback, such as survey replies and social media chats, brands may learn what makes customers happy or upset and modify their products and services to fit those demands.

There are three main differences between text analytics and sentiment analysis. 

• Early warning signs: Using text analytics, you can get a heads-up when a new topic is discovered in your data; looking at a restaurant’s feedback, for example, if the word “spoiled” suddenly increases, you can quickly look into that area. Whereas with sentiment analysis, a decline in sentiment score shows that some component of your business has made your customers unhappy.

• Analyze different kinds of content: Text analytics reveals the most popular discussion topics. You can monitor which ideas are being discussed, which issues are getting the most attention, and which people are contributing the most to the conversation. Sentiment analysis can apply to non-text content such as video, audio, and imagery–to reveal the content’s positivity or negativity–because someone smiling at you gives you a greater sentiment score than someone scowling. 
• Different internal workings: To process text-based data in the same manner as the human brain processes language, text analysis tools use Natural Language Processing (NLP) technology. By employing proprietary algorithms, it identifies the various components of speech, knows which words and concepts are interconnected, automatically corrects for mistakes, and derives meaning. It can delve deep into a single tweet to determine the underlying patterns and trends. Sentiment analysis examines the meanings of words and phrases and whether they are good or negative. Customer’s feelings can be better understood if all subtopics are discussed separately.

Unify customer feedback sources into an easy-to-use dashboard and make winning decisions truly based on customer feedback with Wonderflow’s analytics platform. Using text analytics and sentiment analysis, Wonderflow is the simplest AI-based solution to turn this vast stream of customer feedback into winning decisions.

With Wonderflow, you can: 

• Detect product issues: Identify hundreds of different topics mentioned by customers in their product reviews.
• Empower market research: Combine the most advanced techniques for matching, cleaning, and deduplicating feedback.
• Keep an eye on the competition: Get insights on products and competitors, as shared by your very own customers.
• Raise your star rating: Learn what product features are most relevant to increase a product’s star rating.

Take your e-commerce to the next level by analyzing data from all over the web, both from your buyers and your competitors.  Request a demo today.