Apr 14, 2022
For a visualization of how the topic modeler works, check it out on our website here. Continue reading to learn more about how topic modelers work and how businesses are using them to stay ahead of their competitors.
It can be overwhelming for your marketing team to sift through thousands of customer reviews and surveys to extract overall customer sentiment and feedback on a new product. Enter Artificial Intelligence (AI)-powered text analysis tools.
Text analytics, or text mining, is a popular AI technology that utilizes Natural Language Processing (NLP) to help users extract insights from unstructured text in documents. NLP and machine learning can pull out insights such as sentiments, language themes, patterns, and key phrases for easier data interpretation.
Text analytic tools are used every day by all sorts of different businesses. One of the most common and well-known text mining technology is the sentiment analyzer. People’s feelings can be difficult to interpret over text, but it can also be incredibly useful to understand how people on social media are reacting to your business or how customers feel about your product in reviews. Getting a human to analyze thousands or millions of social media posts and customer reviews is incredibly time-consuming—but, using AI, we can process all those social media posts and determine whether the sentiment behind them is positive or negative—or even a little bit of both.
Sentiment analysis is a form of supervised learning, which means that it requires a human to first tell it what positive sentiment looks like before it can learn on new data. But with more and more data becoming available, unsupervised learning is becoming increasingly popular, which is a form of learning that requires no human intervention at all. Unsupervised text mining is one of the hottest topics in AI right now, and an example of this is topic modeling.
What is topic modeling?
Topic modeling is a form of text summarization. Topic modelers can analyze hundreds, thousands, or even millions of documents, and break them down into a visualization that can be easily understood with just a glance. And topic modelers don’t stop there—given a new, previously unseen document, the topic modeler will assign it to a topic—so you can see if an unread document is about sports, entertainment, politics, or any other topic you’re interested in.
So, how does topic modeling work?
Let’s say we are analyzing a hundred thousand news articles, taken at random from different journals. Some of these will be about sports, some about politics, and others will be about entertainment. Within these broad categories, there will be more specific topics as well. A topic modeler will read these documents, identify words that are semantically related, and then group them together into a topic. The end result will be a mixture of topics about sports, entertainment, and politics. If we look closer, one of the sports topics might contain words like “ice”, “stick”, “puck”, and “hockey”, while another might contain words like “bat”, “ball”, “pitcher”, and “baseball”. Meanwhile, words like “game” and “play” would be in both topics, and a generic word like “watch” might end up in every topic.
1. Insights on customer feedback, at scale
With a topic modeling tool, companies can effectively utilize customer insights and analyze customer sentiment to drive business development and marketing decisions. Without having to peruse thousands of reviews, open-ended surveys, emails, and social media posts, marketing departments can get to the heart of what their customers want and anticipate their needs in advance. Ultimately, topic modeling can help monitor any negative reviews and identify detractors to protect company brand reputation.
2. Real-time analysis
Since the whole process of topic modeling is automated, immense amounts of data can be converted into actionable insight at any given point in time. Businesses can gain a complete picture of the overall user experience real-time from analyzing swathes of data. They can potentially monitor experiences at every stage of the customer journey and take advantage of every touchpoint as an opportunity to drive sales.
3. Improved customer experience
Topic modeling can also be used to sift through support tickets to identify the main issues and patterns based on term frequency. Conversations can then be tagged and the customer can be routed to the appropriate team for further assistance. For instance, customer support tickets that contain the words “auto-renewal” and “billing issues” can be routed to the accounting department for resolution. In fact, the urgency of customer sentiment in their queries can also be detected so that unpleasant situations can be diffused in a timely manner.
For businesses to stay competitive in today’s informationally dense society, topic modeling is one avenue for saving companies time and money. Imagine being able to read thousands of documents in 1/100th of the time and extracting key text insights simultaneously. Topic modeling is a powerful method for mining copious amounts of data and fetching the information businesses are seeking in a short amount of time. Information retrieval and analysis no longer have to be manual for marketing and business development teams.
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