Text mining, also known as text data mining or text analytics, refers to the process of deriving high-quality information from text. Leveraging techniques and tools from both AI (artificial intelligence) and NLP, text mining involves the discovery of patterns, trends, and insights in text data. Text mining is widely used in various fields, including marketing, business intelligence, healthcare, finance, to make sense of large amounts of unstructured text and derive actionable insights. 

How Text Mining Applications Benefit Your Company

Text mining can provide numerous benefits to a company across various departments and functions. Here are some of the key ways it can add value:

  1. Customer Insights and Sentiment Analysis
  2. Market Research and Competitive Analysis
  3. Improving Customer Service
  4. Enhancing Product Development
  5. Boosting Marketing Efforts
  6. Human Resources and Employee Insights
  7. Knowledge Management
  8. Operational Efficiency

By leveraging text mining, companies can unlock valuable insights from unstructured text data, leading to improved decision-making, enhanced customer experiences, and increased operational efficiency.

What Are the Main Steps in the Text Mining Process?

Text mining typically includes the following tasks:

  1. Information Retrieval: Extracting relevant information from large text collections, such as documents, emails, web pages, and social media posts.
  2. Natural Language Processing (NLP): Using computational techniques to analyze and understand human language. NLP includes tasks like tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
  3. Text Categorization: Automatically classifying text into predefined categories or topics. This can be used for organizing documents, spam detection, and more.
  4. Text Clustering: Grouping similar documents or text segments together based on their content. This helps in identifying themes and patterns within large text datasets.
  5. Sentiment Analysis: Determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. This is commonly used in social media monitoring and customer feedback analysis.
  6. Topic Modeling: Discovering abstract topics within a collection of documents. Techniques like Latent Dirichlet Allocation (LDA) are commonly used for this purpose.
  7. Information Extraction: Extracting specific pieces of information, such as names, dates, and relationships, from unstructured text.
  8. Summarization: Creating concise summaries of large texts to highlight the most important points.
  9. Text Visualization: Using graphical representations to help understand and interpret text data, such as word clouds and topic maps.

Text Mining Examples in Marketing

There are many use cases available for text mining. If you were in marketing, for example, here are some of the most common use cases you might consider.

  • Learning about positive, negative, and neutral reactions from your audience: Sentiment analysis is an excellent tool for marketers as it allows you to quickly see what the reception is to the topic that you’re studying. When you have a good understanding of your audience’s reactions, you can tailor your marketing based on that information.
  • Categorizing survey responses: Group survey responses into broad topics or get granular with it, depending on your needs. You can focus on the areas that are most important for a particular campaign. Recurring themes may require closer examination, so you can conduct more studies that focus specifically in those areas to get more information.
  • Translating and scoring survey results: Are you working with more than one language on your survey responses? You don’t need to translate that as a separate step before it goes into your text mining application. Simply choose a software that supports the languages you see the most and it can automate the process.
  • Gauging interest in a new concept: Even when you do your best at developing a concept that should appeal to your audience, sometimes the latest project just falls flat. You can start to troubleshoot why that happened by using text mining and open ended survey questions to see what your audience is thinking about the latest products, services, and company moves. By gauging the interest in a new concept before you move forward with the project, you can handle development much more cost-effectively. This helps you avoid particularly high-profile failures, as a small study may end up with respondents that are more on-board with the concept than a more representative sample of your audience.
  • Understanding the customer experience: Do you know why your customers feel the way that they do about your customer experience? It’s not enough to know if they are happy or not. You need to know the why behind it if you want to excel at marketing. Text mining gives you the why so that you can continually improve the experience and the marketing tools that support it.
  • Discovering your customer satisfaction ratings and the meaning behind them: Your audience gives you a lot of feedback on whether they’re happy or not, you just need a way to analyze it. Use text mining to look through customer service records to identify customers who may be open to purchasing again, those that are upset with the company and need attention, and others that may need a push to move away from being ambivalent in either direction.
  • Tracking the success of new products and services: You want to know how well your new products and services are doing now, not weeks or months from now. Automating the analysis through a text mining tool means that you can get near a real-time understanding of how well a product launch is going.
  • Finding new business opportunities: Open ended survey responses allow you to find replies that are outside of the norm. Sometimes your customers have adopted a product or service for a use case that never came up in research studies. Expanding horizontally or vertically may be possible based on this data, which can offer an excellent approach to building your business.
  • Using customer service data for marketing strategies: Your customer service data is a marketing goldmine, but it’s often overlooked due to the logistical challenges of processing the information. Text mining eliminates these concerns and allows you to find out more about your customers, what they like, dislike, and how to keep them loyal and happy.
  • Providing hard data for reports and presentations: If you need a way to make your case to upper management, having powerful visualizations in helpful reports and presentations is one way to make it happen. Text mining creates structure out of unstructured data, so you’re able to use it in this fashion. Customizable dashboards are another way to easily access the data in a form that’s user-friendly for most marketers. When you can easily work with the data, that makes it more accessible to power all types of marketing efforts.
  • Improving the value of social media comments: People are more than happy to comment on social media posts, but harnessing that data is hard if you’re doing it manually and have a relatively active page. Text mining makes this process more efficient and allows you to leverage such a large and frequently updated data set. Consistently looking at your social media comments is also a good way to stay ahead of any public relations problems you may encounter. You can execute your crisis communications plan as soon as you start seeing negative comments pop up.
  • Creating performance benchmarks for marketing campaigns: Get more benchmarking metrics for your marketing campaigns so you can study how customer sentiment changes over time, the ways they react to new campaigns, and isolating the characteristics that lead to a successful marketing effort.
  • Powering Voice of the Customer programs: Voice of the Customer programs are greatly improved when you have a cost-effective and productive way of working with audience feedback.

Whether you’re using text mining for a one-off study or an ongoing series, your team will benefit from its implementation. It takes some time to fine-tune the results for your use cases, but once you get it dialed in, you’re going to wonder how you ever did without it.

Choose Ascribe For Your Text Analysis Needs

Ascribe has two advanced text analytics solutions to meet your business needs. CX Inspector is a text analysis solution that quickly unlocks actionable insights from large data sets with unstructured or open end responses and creates charts to visualize the results. Coder, another text analytics solution, is the leading verbatim coding platform designed to improve the efficiency of coding. Contact us for more information or request a demo with your data.