Analyzing Data: What Are Text Mining and Text Analytics?

As businesses expand, they may undertake large volumes of unstructured text that could lack a natural language or format to derive insights and trends. That's where text analytics can help a growing company. By combining machine learning, statistical, and linguistic techniques, businesses are able to exploit the enormous content at their disposal for making crucial decisions. It's often intermingled with text mining to help organizations get ahead of the game. Let's take a closer look at how mining and analytics can combine forces for better business functions.

Text Mining vs. Text Analytics

Text mining and text analytics are often used interchangeably. The term text mining is generally used to drive qualitative insights from unstructured text, while text analytics highlight quantitative results.

Text mining is used to take actionable insights from customers based on analysis of things like social media posts or online reviews. Whereas, text analytics is used for deeper insights into the unstructured text. Analytics get through this information to understand where the negative experience is coming from when dealing with customer service or the reaction to a product.

An enormous amount of text data is generated daily in the forms of blogs, tweets, posts, and more. Besides, most customer interactions are now digital, which creates another huge text database. Most of this text data is unstructured and scattered, but it can offer valuable knowledge when gathered, collated, and analyzed correctly.

Steps Involved in Text Analytics

Text analytics is a sophisticated technique that involves several steps to take before gathering and cleansing unstructured text. There are different ways in which text analytics can be performed. This starts with combing through various internal databases and external sources through a gathering system to determine the text that needs to be accessed for processing in real-time. Once this information is available, text preparation begins using natural language processing.

After the preparation of unstructured text data, text analytics techniques can now be performed to gain insights faster than ever. Text classification and extraction are the more common forms of this, particularly with certain tags being assigned through text mining techniques.

Classification often is done using machine learning-based systems. In rule-based systems, humans define the association between language patterns and tags. Meantime, extraction finds structured information from the unstructured input text. This is done through a regular expression, a complicated method to maintain when the complexity of text analysis.

Techniques and Use Cases of Text Mining and Analytics

There are a variety of use case scenarios where text mining and text analytics are utilized.

Sentiment analysis is a common form of data analysis post-preparation. It identifies the emotions conveyed by unstructured text in a business intelligence system.

The categorization technique is used for a more fine-grained approach by data analysts. This is often relied upon to prioritize customer service issues based on the severity of the issue. With proper interactive reports and text mining algorithms, this can be accomplished with ease thanks to the advantage of the latest features.

Topic modelling in text analytics finds major themes and trends in a massive set of documents by identifying keywords in text. There's also named entity recognition, or NER, which identifies entities like people, places, and organizations by extracting nouns for better information retrieval.

Text mining and text analytics can be great for taking one's own words and better analyzing for business decisions. It's important to make sure that the documents that are being utilized are being clearly inspected to weed out the unwanted material and make for a better flow for better summarization.

Reference Links:

• Text analytics
• natural language processing
• Analytics & Collaboration