Text data is one of your most valuable assets when looking to understand consumer behavior, gain valuable insights, and grow your business. Enterprises create over 2.5 quintillion bytes of data daily, which is only going up.
However, one of the most prominent challenges organizations face is turning this seemingly endless data into actionable insights.
Extracting, sorting, and analyzing text from different sources can be difficult and time-consuming; this is where text classification or text classifier tools come into play.
As a result, many organizations are turning to Machine Learning-powered Natural Language Processing (NLP) to automate text organization and analysis.
Instead of spending valuable time sorting through text manually—text classification speeds up this process and uncovers insights that enable you to improve your operations, customer service, and product.
We’ve put together this guide to take you through the nine best text classification examples—helping you make the most out of your untapped business and customer data in an automated and easy way.
So, let’s dive right in.
1. Use text classification for social listening
Don’t confuse social listening with your regular social media monitoring. Where social media monitoring notifies you about a specific issue with a quick fix, social listening helps you get to the root cause and understand the customer’s sentiments behind comments or feedback.
Whether it’s tweets, Facebook comments, direct messages, online reviews, or more—it’s easy to get lost in this sentiment analysis model. This isn’t ideal, considering that 83% of customers who complain or comment on social platforms expect a response the same day, and 18% expect it immediately.
Machine Learning algorithms in text classification cut through the noise—helping you prioritize urgent issues like customer complaints, timely requests, or hot-right-now news articles.
Social listening isn’t just about identifying immediate issues—this Machine-Learning model can also be vital for uncovering opportunities and trends you didn’t know existed. For example, social media trends, discovering potential audiences and influencers for marketing, and optimizing messages to meet your engagement goals.
The best part? Unlike many other text analysis tools, at Levity, you can use our social listening model across your entire organization and teams instead of paying on a ‘per seat’ basis.
2. Categorizing customer support tickets
Your customer support (CS) team spends hours dealing with customer issues—and part of their job is to ascertain which team member is best suited to help each customer. If it’s a billing question, they’ll need finance-focused support. They’ll likely want to speak to a customer support rep if they have a complaint.
Manually sorting through customer tickets to decide what they’re about and who can help is a hefty task that takes up a large portion of your team’s time. Imagine there was a way to automate customer ticket allocation by requirements. Imagine if you introduced machine-learning models to your CS tickets.
Text classification for customer support tickets helps support teams avoid hours of manual data processing and improve efficiency by automating ticket management. The text classification model sorts through tickets and allocates them to one of your chosen labels.
For example, in the above AI flow, we can see that tickets are sorted into four categories: billing, feedback, question, and complaint.
Of course, you’ll need a certain amount of test data and validation data for these text classification tasks. However, you don’t need to feature engineering teams in the process to get your ducks in a row. Instead, you can build your own training set and get a multi-class classification on the go with zero coding knowledge in your pocket.
When you categorize support tickets through text classification—it saves customer support agents time, helps them keep up with urgent tickets, and enables them to deliver quick and quality responses to the customers.
3. Customer feedback sentiment analysis
Sentiment analysis—the most popular text classification example—helps you understand the tone of a customer’s feedback and the emotions behind their message.
Its primary aim is to analyze customers’ sentiments automatically—typically as positive, negative, or neutral—to derive actionable insights, understand customers better, and make more informed decisions.
You can use sentiment analysis to:
- Prioritize customer support tickets: using sentiment analysis to detect positive and negative tickets helps prioritize the urgent and negative feedback.
- Monitor social channels: to identify the sentiment behind social media posts and comments and address them accordingly.
- Perform market research: quickly analyze competitors’ product reviews to find and fill gaps in the market.
Understanding customer sentiment is a crucial strength for businesses and can be significantly improved with AI. It enables you to better meet customer needs before, during, and after they arise.
4. Product review classification
Analyzing product reviews is another essential aspect of understanding customer insights and improving their experience. An online product review allows customers to share their experience with both you and other customers or potential customers.
However, analyzing different product reviews and matching them to specific features can take hours. It’s time to get that training data on the go and rely on machine learning to make this task more scalable.
Text analysis for product reviews lets you quickly identify feature-specific insights and feedback from various sources. A training dataset for this could be a Facebook comments section, incoming support emails, or under a specific hashtag.
A great example to help streamline this process is by using this AI model to sort and filter customer insights by product. You’re then able to allocate the right team member to the case, flag any product reviews you may be interested in to your product team, and marketing can even lean on this filtered data to help align on product messaging.
In all, when it classifies text based on the Machine Learning algorithm, it provides instant insights into customer feedback to the relevant people—making it easier and faster to evaluate customer satisfaction with your products and additional feedback on feature improvement.
5. Content moderation
Content moderation can be challenging, time-consuming, and mentally exhausting for employees and businesses—especially if they do this practice manually.
Previous reports have claimed that moderators make 300,000 content moderation mistakes every day. The current content moderation system is not only time-consuming and exhausting—but it’s also flawed. Such mistakes result in customer frustrations and risk your company’s reputation.
You can use AI-powered content moderation algorithms to moderate user-generated text or images without needing a content moderation team. This limits the scope of error and reduces the content your team needs to monitor manually.
For example, you can leverage text analysis to identify hate speech, profanity, NSFW content, and low-quality content. Each of these instances can then be dealt with accordingly without manually finding and categorizing the content.
Text analysis makes content moderation less frustrating and more efficient—allowing you to maintain your brand reputation and remove any harmful content.
6. Document classification
Another way your organization can leverage AI-powered text classification is with document classification models. Document classification helps businesses automatically tag and classify incoming documents.
You likely have to deal with hundreds of different documents daily, including invoices, contracts, orders, and more; not to worry, these are all tremendous potential test datasets for your AI workflow. While managing incoming files can be a simple task to begin with—it gets a lot trickier as you grow and the document volume increases.
Instead, AI-driven automated document classification is much more cost-efficient, faster, and more accurate.
Levity enables you to train to design an AI model for categorizing incoming documents based on their contents and making decisions accordingly.
7. Email response classification
Inbox management wastes time that could be spent on potential prospects. Using text analysis to categorize emails efficiently enables your team to hit the ground running to get their jobs done. They don’t waste time reading through emails that don’t need their attention—they only receive the ones that need their expertise.
For example, you can use email response classification tags—such as interested, out-of-office, delivery error, and not interested to get notified of urgent and interested leads, aggregate happy and satisfied customer responses, and automate polite responses for uninterested outreach emails to save you time and effort.
8. Survey response analysis
Surveys contain numerical and text data—and most companies often focus only on numbers. This is due to the unstructured nature of the text data and the hassle of analyzing text over numerical—until now.
Qualitative survey answers provide a better opportunity for customers to develop their thoughts on your product and brand. It’s traditionally been harder to manage and evaluate, but text classification is speeding up the process.
For example, let’s say you include a question on your survey: “What stands out to you about our brand?”
You get hundreds of replies varying from “I love the Instagram stories you post” to “the support team is super friendly.” Unfortunately, alongside the positive feedback, you undoubtedly get some negative comments too.
Text classification for survey analysis helps identify what your respondents are talking about for you to take action. Take a look at the below survey analysis model:
The text comes indirectly from your survey tools of choice; it’s sorted into three categories—product, marketing, and customer service—and it’s sent to a different platform or team member depending on its category.
For example, survey replies regarding the product can be compiled into a Google sheet for further analysis by your product team.
It’s simple and easy to harness AI to analyze survey responses—and can help speed things up for your team.
9. Text message analysis
SMS Marketing is more important than ever, allowing companies to get up to 98% open rates. It provides significant benefits but also increases the hassle of managing SMS channels, incoming and outgoing message data, and analytics.
This is where text analysis software comes into play. Text analysis can categorize incoming messages based on the customer’s intent, sentiment, and urgency.
As a result, it helps you handle customer queries better and faster. You can also direct the responses per specific categories like complaints, replies, and refunds to the right employee—enabling handling responses with the right mindset.
You can make your SMS inbox brighter through automation and handle many messages effortlessly. Thus, whether you need to categorize and analyze text messages, and images, or classify documents, text analysis is the solution.
Get started with text classification with Levity
The more data you have coming through, the easier it is to make mistakes. These can be minor, or they can be primary—is it worth the risk?
Customers today expect faster and more effective responses on emails, social media, text messages, and more. Text classification provides a wide range of use cases—helping you improve your operations, customer service, and product development. In addition, it saves your team time and can help detect urgent issues before they arise.
Gone are the days when AI was inaccessible to small and medium-sized businesses. No-code AI solutions enable businesses of all sizes to harness machine learning in their day-to-day operations.
If you’re looking to speed up your processes with text classification and more—try Levity today.
Its easy-to-use AI training process and countless integrations enable you to build AI flows that automate tasks you don’t need to spend time on. It’s simple and intuitive—and it’s the future of business.
