Text analytics (also known as text mining or text-formatted data mining) is the process of extracting useful information from large amounts of textual data

by using statistical and machine language processing techniques. Text analytics allows you to identify key topics, assess mood or emotions, identify trends, and develop forecasts.

For businesses, text analytics can be an important tool in solving various tasks. For example, it can help businesses identify customer needs and problems, track and analyze brand awareness and credibility on social media, forecast demand for goods and services, and optimize management processes.

Text analytics is also used for research, as it allows you to find and analyze information in large text datasets, which can be difficult to do manually. This allows us to draw more accurate and representative conclusions, as well as develop new theories and hypotheses.

Thus, text analytics can be an important tool for solving various issues in business and research. It allows you to quickly and efficiently process large amounts of text data, identify key topics and trends, and make forecasts.

Let’s say you run an online store and want to understand what customers think about your products. You can use text analytics to analyze customer feedback on your website, social media, forums, and other online sources.

For example, you can use text analytics tools to automatically analyze text messages and highlight keywords and phrases. These tools can recognize positive and negative reviews, as well as identify common recurring themes, i.e., trends.

You can also understand which products usually receive positive reviews and which ones receive negative ones. Based on this data, you will understand how to improve the quality of your products and positively influence the level of customer satisfaction after interacting with your store.

Use text analytics to track trends and changes in customer opinions about your brand and products. This will give you the ability to respond quickly to any problems and changes in customer sentiment, which can help increase your competitiveness and boost sales.

NovaTalks also provides the function to connect text analytics and automatically separate important information from customer messages in messengers. To do this, you just need to notify a NovaTalks agent, with whom you will work together in detail on the tasks to be performed by the analytic system and organize the connection process.

4 steps in working with text analytics:

1. Data collection: Customer requests can be collected from various sources, such as email, chats, social media, or phone calls.
2. Data processing: Textual data can be processed using text analytic tools such as machine learning algorithms to distinguish requests from complaints or suggestions, and to identify the topics and issues that customers are most interested in.
3. Data analysis: The collected data can be analyzed to identify trends and patterns in customer requests. For example, a company will be able to understand which issues are most common among customers and which suggestions or ideas are most supported.
4. Decision-making: The information obtained from the analysis of customer requests can be used to make decisions on improving customer interaction, solving quality-related problems, and identifying trends. For example, a company will be able to develop new products or services to meet the needs of its customers or improve existing products based on identified shortcomings.

Examples of text analytics usage:

1. Marketing and advertising: text analytics can help analyze advertising campaigns and understand which words and phrases attract the most attention. It can also be useful for analyzing social media to find out which topics and trends are of most interest to your audience.
2. Customer service: Text analytics can help companies track and analyze customer feedback to understand what issues and suggestions are most common and how they can be addressed. It can also be useful for improving customer experience if used to analyze chats and other customer communication channels.
3. Finance: Text analytics can help financial companies analyze financial reports to identify risks and potential investment opportunities. It can also be useful for analyzing news and other sources of information to identify trends and changes in the market.
4. Operations and management: Text analytics can help companies analyze internal communications and reports to understand which processes are working well and which ones can be improved. It can also help track various performance indicators, for example, by using text analyzers to monitor how employees perform their duties and interact with each other.
5. Media and entertainment: Text analytics can be useful for analyzing viewers’, listeners’, and readers’ feedback on movies, music, TV shows, books, and other entertainment products. It can help identify trends and popularity of certain genres and creators, which will help increase the efficiency of media and entertainment companies.