Companies have never had more data to process. More text is processed in modern companies than ever before. While certain procedures, like legal and accounting-related data, need skilled individuals with years of domain knowledge, others require basic kinds of grouping, filtering, and analyzing.
Natural Language Processing (NLP) and Machine Learning (ML)—both subsets of Artificial Intelligence (AI)—are two of the most promising technologies to emerge in recent years. These technologies can perform text classification—intelligent categorization of text, based on its sentiment.
Text classification is a valuable NLP task that helps solve a variety of business challenges. Many of these concerns are related to data management, such as emails, messages, support requests, and more.
The process is done automatically, saving a lot of time and making companies more productive. At the same time, companies can get valuable insights that help them make smart decisions.
Continue reading to discover more about text classification, how it works, and how to get started with your own text classification process in a matter of minutes.
What is text classification?
Text classification is a Machine Learning approach for automatically categorizing open-ended text into a number of predetermined categories. Text classifiers can structure, arrange, and classify almost any type of text, including articles, medical research, and customer tickets, as well as text found on the internet.
Unstructured data accounts for 80 to 90% of data created and gathered by businesses, and its volume is continuously increasing—several times faster than that of structured databases. It’s difficult to extract useful knowledge from this sort of data unless it is arranged in such a manner that enables the detection of the main points.
The traditional way of processing this data is do it manually. However, this takes up a large portion of employees’ time and can be very expensive.
Here’s where automated text classification tools come to the rescue. They combine NLP and Machine Learning to structure and analyze enormous amounts of text in a time-saving and sustainable way.
This means that you can classify articles based on their topics, or organize support requests according to the problem they’re trying to tackle. You could also evaluate your brand sentiment by analyzing the tone of social media posts talking about your brand.
For example, if someone has tweeted: “The product is very user-friendly and simple,” the Text Analysis tool could recognize user-friendly and simple, and assign them as relevant positive tags.
Why text classification is important
With text classification, businesses can make the most out of unstructured data. Text classification tools allow organizations to efficiently and cost-effectively arrange all types of texts, e-mails, legal papers, ads, databases, and other documents. This enables them to save time and make informed decisions based on relevant data.
For example, you could collect app crash reports and categorize them based on the problem. Categories for this could be:
- Loading time
- App not responsive
- Screen freezes
Reasons to consider text classification
Text classification can help you with:
Identifying problems users have with your product
Most customer service requests end up in a backlog, while the product team is prioritizing new features. With a structured system to categorize requests, you’ll have a better overview of the problems users are facing.
Recognizing user segments to improve your targeting
You may segment your audience depending on the words and phrases they use, allowing you to develop more focused campaigns.
Getting ideas for new features
One of your users could tweet “If this product would have a logo generation feature, it would be perfect for me.” This is valuable feedback and you can leverage it to make your product more useful.
Analyzing data in real time
Automated text classification can track your brand mentions in real time, allowing you to see timely posts and take immediate action.
Eliminating human error
Humans aren’t machines and they are prone to errors. Machine Learning examines all data and outcomes through the same filter and parameters. Once correctly trained, a text classification model works with unbeatable reliability.
How does text classification work?
Text classification can be done in two ways: manually or automatically. A human observer evaluates the substance of the text and categorizes it properly in manual text classification. This technique can provide excellent results, but it's very time-consuming and costly.
Automatic text categorization combines Machine Learning, Natural Language Processing, and other AI-based approaches to categorize text more quickly, efficiently, and accurately. We'll concentrate on automated text classification later in this guide.
Although there are several ways to automate text classification, they all fall into one of the three categories:
Using a variety of language principles, rule-based techniques sort text into structured categories. These rules tell the system to identify relevant categories based on their content using semantically relevant text components. Rules are made of two components:
- A pattern
- A projected category
For example, let’s say you want to classify the articles in your website’s blog section into Industry and Product. What you need to do is define a list of keywords that are related to each of these categories.
So, for Industry, you could add tech, tech news, web3, blockchain, IT landscape, market state, etc. For Product, you could define words like product release, app update, bug fix, feature release, and similar.
You may now establish rules for categorization, such as a particular amount of keywords from one of the two categories or a specific ratio of terms in the two categories. The articles can then be allocated to the appropriate category using these criteria.
Now, when you publish a blog called “The market state in blockchain for 2022,” it will be categorized in the Industry group because it has recognized the pre-defined keywords.
Although rule-based systems might seem easy to set up, they can take some time to get them running as testing is required to achieve accuracy. Plus, creating these rules could be complex as you need a lot of domain knowledge for the specific categories you’re defining. However, once testing is complete, the time you would have once spent on manual categorization is now free to spend on other more worthwhile areas.
Machine Learning-based systems
Machine Learning text classification creates classifications based on prior observations rather than depending on human-constructed rules. Machine Learning algorithms understand the varied correlations between bits of text as well as understanding that a specific output is anticipated (i.e., tags) for a specific input by utilizing training data (i.e., text). When we use the term tag in this content, we mean a defined group or category into which any text can be placed.
This next section is a little more complex, but having this information is a great advantage when it comes to learning more about this topic.
To train your Machine Learning classifier, you first need to do feature extraction. Which means converting pieces of text into a mathematical structure in the form of a vector. One of the most common ways is the bag of words, in which a vector reflects the frequency of a word in a predetermined lexicon of words.
Let’s say that your lexicon contains these words: feature, sun, flowers, love, and never. If you have to analyze this sentence: I love this feature, the vector representation would look like this: (0, 1, 0, 1). To get an accurate model, the Machine Learning algorithm needs to be fed with training data that contains feature vectors for text examples and tags (categories).
The model can begin to produce precise forecasts once it has been taught with enough examples. Machine Learning-based text categorization is more accurate and quicker than human rule-based methods. Newer examples can always be labeled to learn new text classifications as the classifiers are easy to manage.
These are some of the most popular Machine Learning algorithms for text classification:
- Support vector machines
- Deep Learning
- Naive Bayes classifiers
There are also combination systems that represent a hybrid between rule-based and Machine Learning-based text classification systems, which generate even more precise results.
These hybrid systems can be improved simply by adding special rules for opposing tags that the underlying classifier hasn't adequately described. The combination also considerably reduces the work required to label data.
Examples of text classification
To better understand how text classification works, let’s take a look at some examples.
The method of evaluating whether a piece of data reflects a positive, negative, or neutral attitude toward a subject is known as Sentiment Analysis. Put simply, Sentiment Analysis understands the emotions expressed in a text.
Here are some business areas where Sentiment Analysis could be useful:
Monitoring brand sentiment on social media.
Analyze the sentiment of social media posts. Examine tweets or Facebook comments to see whether people are commenting favorably or critically about your product/company.
Sorting customer support tickets.
Identify dissatisfied customers and prioritize these tickets.
Analyze survey responses.
Assess the sentiment of what your customers are saying in their survey responses and discover their pain points.
Examine the reviews of competitors’ customers to identify market gaps, which could be your business opportunities.
Language detection is another form of text categorization that is used for a number of applications. These classifiers can recognize the language used in textual data and do a variety of tasks.
If you’re a company that’s present on the global market and has local teams, language detection is the perfect tool for you. For example, you could be directing customer support tickets to the team in the language they’re written in.
Moreover, you can use text classification to sort documents and make data management for your local teams easy. You could also filter off the messages written in a language that you’re not using in your operations.
Customer feedback trends
Analyzing product reviews, NPS ratings, and survey replies to find trends and patterns is a time-consuming and tedious procedure. But, Machine Learning models can help out here too.
Machine Learning can automatically detect semantic relationships in customer feedback and classify the messages by subject and tone, allowing you to focus on the subjects that your customers are talking about the most and learn how they're discussing them.
Customer support tickets
A large portion of support teams’ working hours goes to categorizing typical inquiries, keeping track of unresolved problems, and figuring out the biggest pain points of customers. Thanks to text classification, they can save hours and eliminate this manual lift.
Automation can also boost worker productivity by allowing them to focus on the most important cases immediately, automatically redirect messages to the right colleagues, and send automated answers based on categories like subject, importance, and emotion.
Online content moderation
Online content moderation is a method that establishes pre-determined norms and regulations to govern and monitor user-generated material. These principles are subsequently put into action, and automated with the help of AI content moderation.
Natural Language Processing algorithms decode emotions and grasp the intended meaning of the written text. Sentiment Analysis, for example, may detect the tone of communication and categorize it as bullying, rage, abuse, irony, and so on, before labeling it as positive, neutral, or negative.
Another AI content filtering tool is entity recognition, which extracts names, places, and businesses. This type of content moderation AI method may tell you how many times your brand has been referenced on a specific website, or how many individuals from a specific geo place have left reviews for your company.
Text classification business applications
There are many business applications of text classification. Keep reading to find out about some of them.
Classify email campaign responses
Every day, the average office worker receives roughly 121 emails. You’ll probably get even more if you send out marketing campaigns regularly. Classifying all email campaign responses could take hours from your day.
Without explicit prioritization, processing responses to outbound emails becomes ineffective. Manually responding to uninterested prospects takes time away from more important leads. It's difficult to evaluate the effectiveness of email marketing if answers aren't classified properly.
Levity uses Machine Learning to tag emails depending on their content. It can classify emails using tags such as 'priority,' 'confidential,' 'personal;' or, by customer segments, or teams that should respond to them.
Ads media analysis
Each month, some firms test hundreds of ad sets. This is not only expensive, but it also annoys potential buyers. Most ad platforms aren't helpful, though, because they profit from A/B tests through ad expenditure.
With AI, you can create a bespoke Machine Learning model based on ad set performance in the past. Instead of your best guess, you’ll be able to put money on the ad that will perform best based on data. Improve conversion rates and reduce spending through an improved ad selection that will perform well thanks to machines.
HS codes are supposed to make international trading easier, yet they may be a hassle for businesses as manual processing and inaccurate data can result in costly errors. This work can be automated using Machine Learning platforms, such as Levity.
Recurrent Neural Networks examine the product's image and content and assign it to the appropriate category.
If you are undecided, it will prompt you to make a choice. Based on the product image and description, the AI platform can automatically categorize items according to HS codes.
The process is fully automated and based on your own data. As a result, you get clean and accurate product categories that lead your customers to conversions.
The administrative burden of obtaining client input is never easy. Even worse, when you finally acquire the necessary paperwork and discover that one signature is missing, it’s a huge bottleneck. Technical issues like these might potentially cause waits that last for days, even weeks.
To solve this problem, no-code Machine Learning tools like Levity allow you to build models to automatically check if the documents you get are accurately filled out. If any inputs are missing, they send immediate alerts so that senders can react before moving on to anything else.
Categorize service requests
Managing a large number of requests on a daily basis is stressful. The larger the service inventory, the more work it takes to schedule efficiently. To help save time, use a platform that can automatically classify requests into predefined categories. Here's what you can do with Levity's platform:
- Tag jobs by service category, employment location, or anything else that makes sense for you.
- Deliver service requests to your staff efficiently according to their locations and expertise.
- Spend less time preparing and organizing service requests and get more time to meet actual customer requirements.
Having relevant insights into how consumers use your product is an important component of being a product-led company. A good prioritization method should identify which features add the greatest value and highlight any areas where value is lost or concealed.
While quantitative data is frequently accessible, qualitative input is frequently required to fully grasp user behavior. User transcriptions, complaint files, customer evaluations, and even social media comment sections may all provide qualitative data. It requires hours of human labor to analyze and attribute them to different product aspects.
Machine Learning provides you with the capabilities to categorize free-text data such as internet reviews, interview transcripts, and social media comments in real time. Sort by topic, sentiment, or whichever criteria are most important to you. Save time on identification, get big-picture insights, and focus on the ones that are the most essential.
Not all emails are relevant or belong in the same category. It's no wonder, therefore, that an overload of emails is a significant reason for data loss and manual processes.
By labeling communications according to their category, email classification can help to clarify the process. For example, communication might be classed as "private," "important," "confidential" or by department or client. You could create rules in the email platform, but this is often not precise enough.
Add value to your Microsoft Outlook or Gmail conversations by adding the label that a human would. It's finally time to organize that "info@" mailbox!
You determine what happens next after Levity’s system is in place:
- Forward to HR.
- Mark as important.
- Move to spam.
- Snooze for later.
Analyze survey responses
Use text classification to examine the information from consumer survey responses. Automate this process and save your company weeks, if not months, of manual effort. You can conduct the following analysis with Machine Learning software:
- Positive, negative, and neutral responses are automatically detected.
- Filter for urgency.
- Understanding the feedback's topic.
Sentiment Analysis can be used to rapidly learn the tone of written survey feedback. This can assist you in a variety of ways, like tracking consumer responses and feelings on specific changes, learning how customers are using a particular feature, and so much more.
How to create a text classification workflow in 4 steps
Now that you’ve learned what text classification can do for your business, let’s see how you can create a text classification workflow with Levity.
1. Create an AI block
To get started with your text classification workflow, the first thing you need to do is to log in to the Levity platform and click the Create an AI block button.
Here, you want to choose the Text Classifier, if you have plain text, or the PDF Classifier, if your data is in a PDF format.
2. Upload training data
The next step is to add your data to the system. Your data could be:
- Internal: from a text file you already have, such as a Google Sheet.
- External: from other integrated apps, such as your CRM tool.
From here, you’ll enter the Organize Data phase, where you’ll be able to add tags to your validation data.
3. Train your AI block
After you’ve tagged your data, it’s time to train your AI block. Just hit the Start training button.
4. Test your AI block
After the training is completed, you’ll see how your AI block performed. Now, you can add a test dataset to examine its accuracy.
5. Connect your AI block
Congratulations! You have your AI block. Now, you can connect it to your existing workflows by integrating it with apps you’re already using. Combine tools like Dropbox, Airtable, Salesforce, Intercom, and many others.
6. Watch your AI block improve over time
Now, all that is left is for you to sit back and watch how your AI block improves over time. When unsure, your AI block will ask for more data and learn from it automatically.
Learn more about how to create your own AI Text Analysis that relieves you of everyday repetitive tasks, allowing your team to achieve greater levels of productivity. Want to build your text classification model immediately? Get started now.
Top text classification resources
To get started with test classification, you need two types of resources: a training dataset and a text classification tool. Let’s see why you need them.
Datasets to train your model
Datasets will serve to train your AI model. Only by learning from past experiences can Machine Learning algorithms produce accurate predictions. When a Machine Learning model gets in touch with instances of properly labeled data, it uses that data to create predictions on unseen textual data.
When it comes to internal data, you can use:
- CRM software inputs. (HubSpot, Salesforce, Zendesk, etc.)
- Collaboration tools. (Slack).
- Survey responses. (Typeform, Google Form, Survey Monkey, etc.)
- Chat conversations. (Intercom, Twillio, HubSpot, etc.)
You can also use data you already have as a CSV file.
If you don’t have internal data or want to use other sources, you can also use data that are available on the internet. You could scrape this data, or get it via APIs and publicly available databases. Here are some popular databases widely used in Data Science:
- Google-Landmarks-v2 - for landmark recognition.
- SOCR data — Heights and Weights Dataset - contains the heights and weights of a number of humans.
- Credit Card Fraud Detection Dataset - for fraudulent transactions made with credit cards.
- Wine quality dataset - contains a lot of information about different wines.
- IMDB-Wiki dataset - for face images with labeled gender and age.
- Twitter US Airline Sentiment - useful Twitter data on US airlines.
- Rotten Tomatoes Reviews - over 480,000 movie critic reviews.
You can find more training datasets here.
A text classification tool to automate your workflow
Once you have the training data you need, the next step is to find a text classification tool that suits your needs. There are many open-source text classification tools with Python, Java, R, and others, but these require more complex technical knowledge to use.
For non-technical people, the best solution is to use a no-code tool. No-code text classifiers don't require any prior knowledge of Machine Learning, and even non-programmers can employ and interpret text classifiers.
When it comes to creating your text classification system, delegating the heavy job to a no-code text classification tool can save you time, money, and energy. Giving you time to focus on areas of your business that will benefit from a human touchpoint.
Get started with text classification today
Text Analysis is a new topic of research in general. The method of evaluating and extracting insights from unstructured textual data is already being used in fields including marketing, product management, education, and administration.
Text classification is an excellent tool for creating cutting-edge workflows and managing company data. In order to provide important insights and drive business choices, text data must be transformed into quantitative data. Automating the manual process of text classification leaves you more time to spend on essential business and growth tasks.
Text categorization helps organizations discover serious concerns sooner and more effectively, which is critical when consumers want faster and more effective actions.
With Levity, you can get started with text classification in minutes. No technical knowledge is required. Integrate your text workflow into your existing processes and save time for meaningful work.