Text analytics is the process of analyzing unstructured data such as text to find patterns and insights. It includes natural language processing (NLP), which is a field of computer science and artificial intelligence concerned with the understanding of human language. Text analytics can be done manually or through software developed by a text analytics company that automates the process.
In recent years, the field of text analytics has grown exponentially. Businesses of all sizes are now using text analytics to make better decisions, improve customer service, and streamline operations. However, with so many different applications for text analytics, it can be difficult to know where to start. In this article, we will explore some of the most useful business applications for text analytics. Keep reading to learn more.
Text Categorization
Text categorization is the process of automatically assigning a category to a given piece of text. This can be used for tasks such as content management, spam filtering, and sentiment analysis. Text categorization is achieved through the use of natural language processing algorithms that analyze the text for certain features that are indicative of a particular category.
One common approach to text categorization is to break the text down into tokens and then classify each token according to its type. Tokens can be words, punctuation marks, or other symbols. There are many different ways to break down text into tokens, and each method has its own advantages and disadvantages. Some methods are better at detecting certain types of tokens than others. For example, lemmatization is good at detecting words but may not be able to detect non-word characters such as numbers or symbols.
Once the text has been tokenized, it must be classified according to a set of predefined categories. This is usually done using a machine learning algorithm such as a neural network or support vector machine. The algorithm is trained on a data set that consists of examples of texts that have been assigned to each category. The algorithm then uses this training data to determine which features are most indicative of a particular category and assigns the new text accordingly.
Marketing
Text analytics can be used to improve marketing efforts by identifying positive and negative customer feedback online and in social media. The data can then be used to create targeted marketing campaigns that address the concerns of customers who had a negative experience or reinforce the positives for customers who had a positive experience.
Production
Text analytics can play a role in optimizing business processes by identifying bottlenecks. The data collected can be used to improve the process and make it more efficient. In some cases, text analytics may even be able to predict problems before they happen. This can help businesses to avoid costly disruptions in their operations.
Customer Service
Additionally, text analytics can help identify areas where customer service needs improvement. It can be used to identify what customers are saying about your company, what they are happy with, and where they feel you could do better. This information can help you to focus your efforts on the areas that are most important to your customers and improve your customer service overall.
Human Resources
The use of text analytics for human resources has grown in popularity in recent years. This is because text analytics can be used to screen candidates by reviewing their social media posts for red flags such as racist or sexist comments. The data collected from social media posts can also be used to create better job descriptions that are more representative of the skills required for the position.
Text analytics can also be used to track employee satisfaction. This can be done by reviewing employee satisfaction surveys. Tracking employee satisfaction can help human resources departments identify areas where employees are unhappy and need more support.
Additionally, text analytics can be used to identify and reduce employee turnover. This can be done by analyzing employee exit interviews and the text of employee emails and social media posts. By identifying the factors that lead to employee turnover, human resources departments can put in place policies and procedures to reduce the likelihood of employees leaving the company.
Overall, text analytics is a valuable tool for businesses to make better decisions and understand their customers better.
