8 Applications of Sentiment Analysis

8 Applications of Sentiment Analysis

Sentiment analysis is the automated process of analyzing text to determine the sentiment expressed (positive, negative or neutral). Some popular sentiment analysis applications include social media monitoring, customer support management, and analyzing customer feedback.

In the background of sentiment analysis, advanced AI algorithms apply language deconstruction techniques, like tokenization, part-of-speech tagging, parsing, and lemmatization to break down and make sense of text. Only then can machine learning software classify unstructured text by emotion and opinion.

Automatic text analysis can be performed on any text source, to sort survey responses and live chats, Twitter and Facebook posts, or to scan emails and documents. All this is valuable information for companies, filled with insights that can help them make data-driven decisions.

In this article, we’ll explain how you can use sentiment analysis to power up your business.

Sentiment Analysis Applications in Business:

Customers contact businesses through multiple channels, and it can be hard for teams to stay on top of all this incoming data.

With sentiment analysis tools, however, you can automatically sort your data as and when it filters into your help desk.

Let’s take a look at the most popular applications of sentiment analysis in real life:

Social media monitoring

Social media posts often present some of the most truthful points of view about products, services, and businesses because users offer their opinions unsolicited. They are simply compelled to tell the world how they feel. 

Around 6,000 tweets are sent every second, and a large proportion probably mention businesses. Whichever industry you work in – retail, finance, tech, health, government – you probably receive a lot of feedback on social media. And, you’re looking at hours, maybe even days, to process all that data manually. 

But, with the help of machine learning software, you can wade through all that data in minutes, to analyze individual emotions and overall public sentiment on every social platform.

Example of a positive tweet mentioning Zapier's fast service

Sentiment analysis of social data will keep an eye on customer opinion 24/7 and in real time. You’ll be able to quickly respond when something negative starts circulating and boost your image when you receive positive mentions. And, you’ll get regular, dependable insights about your customers, which you can use to monitor your progress from one quarter to the next. 

Customer support

Customer support management presents many challenges due to the sheer number of requests, varied topics, and diverse branches within a company – not to mention the urgency of any given request. 

Sentiment analysis with natural language understanding (NLU) reads regular human language for meaning, emotion, tone, and more, to understand customer requests, just as a person would. You can automatically process customer support tickets, online chats, phone calls, and emails by sentiment, which might also indicate urgency, and route to the appropriate team.

Try out our sentiment analysis classifier to see how sentiment analysis can be used in customer support:

Test with your own text

Results

TagConfidence
Negative98.1%

Sentiment analysis can automatically mark thousands of customer support messages instantly by understanding words and phrases that indicate negativity.

Customer feedback

Sentiment analysis can also be used to gain insights from the troves of customer feedback available (online reviews, social media, surveys) and save hundreds of employee hours.

Sentiment analysis can read beyond simple definition to detect sarcasm, read common chat acronyms (lol, rofl, etc.), and correct for common mistakes like misused and misspelled words.

Comment 1

“Love the user interface. Setup took five minutes and we were ready to go.”

Comment 2

“Took me 2 hours to set up, then I find out I have to update my OS. Love it!”

Sentiment analysis would classify the second comment as negative, even though they both use words that, without context, would be considered positive. 

Keeping track of customer comments allows you to engage with individual customers in real time. And you can target read for new products or specific user issues.

Brand monitoring and reputation management

Not only that, you can keep track of your brand’s image and reputation over time or at any given moment, so you can monitor your progress. Whether monitoring news stories, blogs, forums, and social media for information about your brand, you can transform this data into usable information and statistics. You can also trust machine learning to follow trends and anticipate outcomes, to stay ahead and go from reactive to proactive.

Voice of customer (VoC)

Combine and evaluate all of your customer feedback: from the web, customer surveys, chats, call centers, and emails. Sentiment analysis allows you to categorize and structure this data to identify patterns and discover recurring topics and concerns.

You can understand your customer base collectively, then segment them to target directly. For example, using data from a customer survey, you might want to offer free services or promotions to entice unhappy customers. Or offer rewards to those that are extremely happy with your company, encouraging them to spread the word about your product or service.

Listening to the voice of your customers, and learning how to communicate with them – what works and what doesn’t – will help you create a personalized customer experience.

Voice of employee

Engage your employees, reduce turnover, and increase productivity. Use AI to evaluate employee surveys or analyze Glassdoor reviews, emails, Slack messages, and more (without feeling like Big Brother). Sentiment analysis software allows you to analyze employee opinions subjectively, with no human input.

Process unstructured data to go beyond who and what to uncover the why. Create analysis models for your specific needs. You’ll discover the most common topics and concerns to keep your employees happy and productive.

Product analysis

Find out what the public is saying about a new product right after launch, or analyze years of feedback you may have never seen. You can search keywords for a particular product feature (interface, UX, functionality)and train sentiment analysis models to find only the information you need.

Discover how a product is perceived by your target audience? Which elements of your product need to be improved? Sentiment analysis provides better results than humans because AI doesn’t alter its results and it’s not subjective.

Market and competitor research

Another use case of sentiment analysis is market and competitor research. Find out who’s trending among your competitors and how your marketing efforts compare. Get a comprehensive view from the ground, from every aspect of your and your competition’s customer base.

Analyze your competitor’s content to find out what works with the public that you may not have considered. You’ll understand your strengths and weaknesses and how they relate to that of your competitors. 

Start Using Sentiment Analysis in Business

Sentiment analysis is one of the many data analysis tools you can use to understand your customers and how they perceive your brand. Machine learning has broadened the horizons of text analysis to perform tasks that were previously unthinkable. The internet is full of useful data about your company, and now it’s right at your fingertips.

Take advantage of all the useful opinions ready to be mined with sentiment analysis.

MonkeyLearn has free tools you can begin using in just a few minutes.

Request a demo. and get started right away

Inés Roldós

April 9th, 2020

Posts you might like...

MonkeyLearn Logo

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

Try MonkeyLearn
Clearbit LogoSegment LogoPubnub LogoProtagonist Logo