Technology is making our lives easier.
Topic modeling is a tech advancement that uses Artificial Intelligence to help businesses manage day-to-day operations, provide a smooth customer experience, and improve different processes.
Every business has a number of moving parts. Take managing customer interactions, for example. Customer service teams deal with thousands of customers every day and may lose touch with essential business tasks while performing mundane and repetitive activities that can easily be automated.
It’s not just customer service that struggles to keep up, but almost all your teams—including finance, HR, accounting, production, and marketing—waste their time on mundane tasks.
What if Artificial Intelligence (AI) could automate these mundane tasks for you?
Topic modeling is one of the many ways in which AI can do just that.
Here's a deeper dive into this time-saving and tactical technique that uses AI to automate processes and save your business time and money
Topic modeling defined
Topic modeling is a type of statistical modeling that uses unsupervised Machine Learning to identify clusters or groups of similar words within a body of text.
This text mining method uses semantic structures in text to understand unstructured data without predefined tags or training data. Topic modeling analyzes documents to identify common themes and provide an adequate cluster. For example, a topic modeling algorithm could identify whether incoming documents are contracts, invoices, complaints, or more based on their contents.
Latent semantic analysis and latent Dirichlet analysis are two main topic modeling methods that analyze large text files to categorize topics, provide valuable insights, and support better decision-making.
Here’s a brief breakdown of both methods.
LSA: latent semantic analysis
Latent semantic analysis is a statistical technique for extracting and representing the main ideas in a body of text. LSA is based on the principle that words that are close in meaning tend to be used together in context.
For example, LSA allows you to create user profiles to provide a more personalized user experience. The organization can use demographic data, purchase history, and previous interactions and behavior to group similar users and establish a set of user profiles.
This is a one-time process, which can then be automated using Machine Learning. How?
LSA links words semantically by context and word frequency—how often a word occurs in a document. In this case, customers with similar attributes—such as behavior and interaction type—will be grouped under one topic (category). Using unsupervised machine learning, LSA automatically creates separate topics based on previous inputs and outputs.
LSA assumes all similar documents to share the same patterns when their word frequency and order are consistent, which helps analyze not just one but a collection of documents. It’s a part of natural language processing, closely related to learning and understanding human language and judgment, and has many applications, including document and image classification.
LDA: latent Dirichlet analysis
Latent Dirichlet analysis is one of the most popular topic modeling methods. It uncovers the hidden structure in a set of observations by looking at the relationships between words in a document and grouping them into topics.
It considers documents as a mixture of topics and topics as a mixture of words. When analyzing multiple documents, it treats them as having similar topics with different distributions.
For example, Document A may have "10% Topic Y" and "90% Topic Z", while Document B has "40% Topic Y" and "60% Topic Z".
The LDA model further breaks down topics into words, assuming that multiple topics may have common words. Consider the following three topics.
- Biology: This document may contain words like anatomy, dissection, and genomes
- Chemistry: This document may contain words such as solutions, chemicals, and alloys
Biology and chemistry may also share words like molecular weight and carbon cycle, which are considered similar “topics” in LDA. With LDA, you can analyze matching topics in these two documents and create specific groups for each topic. This way, you create a document term matrix, an interconnected network of topics, and analyze multiple documents to classify their contents, text, image, and more.
How does a topic model work?
Despite the many mechanics and algorithmic features in topic modeling, topic models work pretty simply. They deduce words, grouping similar word patterns into topics to create topic clusters.
Suppose you’re a retail company selling fashion clothing. Your online store has different categories and product images and descriptions under each category. If you want to show customers similar products or just group products into their related categories, it's impractical to tie each product with its category manually.
To make things easier, you can use a topic modeling algorithm and automatically tag and group items into different categories. The topic model will analyze huge amounts of unstructured data to find patterns based on word frequency, order, distance, and meaning and group various items into relevant categories without predefined training—et voilà!
What is the difference between topic modeling and clustering?
Topic modeling is a statistical technique for discovering latent topics in a collection of documents. Clustering is a Machine Learning method for grouping together similar data points. Both methods group documents, but they differ in how they do it.
Clustering algorithms group together similar items, while topic modeling algorithms identify relationships between items. Topic modeling uses a statistical approach to find hidden topics in a collection of documents.
Clustering is typically used to group items together so that they can be analyzed as a whole.
Topic modeling finds relationships between items and understands a dataset's hidden structure. Additionally, topic modeling doesn’t require manual labeling of the data points, whereas clustering requires manual labeling of the data points.
Topic modeling vs. topic classification
Both topic modeling and topic classification are unsupervised learning methods. This means that they do not require labeled data to learn. They can both be used to discover hidden patterns in data—they just do it differently.
Topic modeling vs. text classification
Whereas topic modeling involves finding topics in a collection of documents, text classification leverages text classifiers to assign a label to a document based on its content.
Text classification is more specific and categorizes documents into predefined categories.
Here are a few key differences between topic modeling and text classification:
Although topic modeling and text classification work differently, they can work together to get the best possible AI predictions.
Levity combines techniques from both supervised and semi-supervised learning to create an AI automation tool that works on unstructured data, including images, documents, and text and allows you to train AI models tailored to your use case.
Here's a closer look at topic modeling and text classification applications.
Topic modeling applications
Topic modeling can be used for a variety of tasks. Here are some of its common applications.
Topic modeling is an unsupervised Machine Learning technique that uses Natural Language Processing to understand the context and label new documents. It automatically tags each document with the topic it most closely resembles.
For example, latent Dirichlet allocation is commonly used to classify scholarly articles by their topics and has also helped classify the New York Times articles.
Tagging customer support tickets
You can also use topic modeling to automatically tag customer support tickets. Start by training a topic model with a labeled dataset of customer support tickets. The topics learned can then be used to flag new tickets.
The Machine Learning technique mines data from support issues your customers have had over the years, and idenitifies the main issues that keep popping up.
This can help support agents quickly spot issues and their frequency to provide the best customer support possible route tickets to the appropriate team, and customers can find relevant information faster.
Detecting urgency support tickets
Topic modeling can easily detect urgency in customer support tickets. Prioritizing requests and inquiries can be a real chore for customer service agents.
Using Natural Language Processing techniques and Machine Learning models, topic modeling can extract topics from tickets, group them, identify the semantic relationship between them, classify tickets into different categories, and flag tickets that need the most attention.
Analyzing customer feedback
Many companies typically don’t focus on analyzing customer feedback due to a lack of understanding of the manual work involved. With topic models, you can easily evaluate customer feedback and even save costs.
Topic models assess customer feedback and create labels based on what customers say. They’re easy to use, fast, and efficient.
Text classification applications
Text classification uses supervised machine learning algorithms to classify text. Let's look at some of its uses.
Analyze survey results
Text classification can help analyze survey results by training a text classifier on a labeled dataset to categorize new results.
In order to ensure business growth, you need to know what your customers and employees think of you. Gathering their feedback manually is tedious, and this is where surveys come in.
Popular surveys like Net Promoter Score (NPS) are critical to your bottom line, but manually analyzing text from surveys is time-consuming. Automating the process can save you from spending hours on a task and reduce the time-to-value.
In order to simplify the process, Levity automatically segments customer feedback by sentiment into different categories such as promoters, detractors, and supporters. It also auto-detects positive, negative, and neutral responses to uncover underlying patterns and understand the intent and subject matter of the feedback.
Levity's sentiment analysis capabilities allow you to analyze customer feedback and automatically route feedback to relevant teams or leaders within your organization, who can take appropriate action to improve the employee or customer experience.
Automatic ticket routing
Text classification comes in handy when automatically routing tickets to an appropriate customer service agent. To manually route tickets, you need a human support agent. This only wastes your support team’s time as most tickets are repetitive.
Machine learning algorithms are specifically supervised and trained to automate the process and reduce errors. Automatic ticket tagging and routing can go a long way in making the support process more seamless and improving the customer experience with quick resolution.
When it comes to automatic ticket routing, Levity is a best-in-class solution that supports your customer service with efficient automation. Automate categorizing tickets and free your agents to focus more on important customers, boosting employee morale, productivity, and response time.
Understanding the sentiment behind a request or feedback and classifying it as positive, negative, or neutral is critical in a customer-centric landscape. It helps you ensure your customer’s needs are met both proactively and preemptively.
Knowing the sentiment of a message can help you deal with dissatisfied or angry customers and provide them with a quick and correct solution.
Levity gives you the power to proactively listen to and understand your customers to identify issues. The AI software extracts the tone of any message even before opening or reading it and categorizes messages based on these tones.
Managing email is a major pain point for most professionals.
It's difficult to manually categorize and tag emails based on content, especially when working with many emails in one inbox. Deciding what to delete, keep, and prioritize can take a lot of time. You may even miss important emails and end up creating gaps in communication.
AI-powered text classification can be of great help here. Email classification can help you classify emails into different categories such as personal, customer, internal, and external.
Levity's AI engine simplifies email categorization for different mailboxes, inducing Gmail, Outlook, and more.
Once the AI model has categorized your emails, Levity enables you to automate the next steps—whether that be to forward emails to a specific team member or move them to a specific folder.
With everything happening online, it’s essential that businesses keep track of brand mentions and how their brand is being projected across the internet. Social listening is critical to maintaining a positive brand image and addressing customer concerns.
That being said, it isn’t exactly easy to manually record everywhere you’re mentioned. Add social media to the mix, and the process becomes even more complex.
Sounds hectic, right?
If you do it manually, it is. That’s why you need to automate the process. Listen to your existing and potential customers and tap into different areas to grow your brand with text classification.
Levity enables you to listen out for certain mentions across the web, from complaints to competitors, to give you a better idea of what your customers are saying about your brand.
Levity enables you to pull brand mentions and reviews from a wide variety of sources, run them through an AI text classification model, and automate the next steps. For example, negative mentions could be sent to the customer success team.
How to do topic classification with Levity
Topic classification—categorizing documents into predefined topics—can seem daunting at first, but with the right tool, you can do it at the click of a button. Levity is an automated no-code AI solution that easily and accurately divides topics into different categories in simple steps.
To use Levity for topic classification, you first need a labeled dataset of documents. The dataset should be in a text or .CSV file, where each line is a document. The documents should be labeled with their categories, such as ‘health’ or ‘finance’.
Train a topic model
Once you have a labeled dataset, you need to train your AI block on it. Levity's AI engine is intuitive and helps train AI blocks faster based on similar inputs across the system.
Your AI block, once trained, prompts you to the next step: improving the performance score. In this case, the AI Block overview will show your model’s performance score and the actions you need to take to improve it. For example, create a certain number of labels or add more data points to enhance the score.
After completing these actions, you can retrain the AI block to get an optimal score and move forward.
Label new documents
The AI block is now ready to label new documents. Once trained and tested, it knows exactly where to go and what to do with all future documents.
AI solutions help you work better
Levity’s AI learns your preferences, habits, and patterns to recommend the most productive tasks for you each day. It also collects data on your performance to give you detailed feedback so you can improve as you go.
The no-code AI solution gives businesses big and small the opportunity to automate mundane tasks and focus on other—more important—tasks.