Text Analytics- All That You Need To Know

Businesses are generating tonnes of data out of which 80% is unstructured. This big data is available in various formats and sizes. Making the most out of this big data can enhance business decision making processes. The data entering a business, or the data generated by a business can be converted into structured formats that can help in making meaningful and actionable business decisions.

The biggest question of today is, how to generate insights from text data (unstructured)?

The answer lies in the hands of one wing of data analytics – text analytics and NLP analytics solutions.

Text analytics is the automated process of converting unstructured text data into structured data to bring out actionable and meaningful business insights.

The words ‘Text mining’ and ‘text analysis’ are often used in place of the word text analytics. But, in the data analytics stage, text analysis, text mining and text analytics work together to bring out actionable inputs.

Text analytics can be done manually, but it is an inefficient method. Automated text analytics model has therefore been developed that uses text mining and NLP algorithms to provide actionable business insights.

A huge amount of unstructured data is present in all business. There are many ways to make a lot from those data. Text analytics software, that work on advanced NLP algorithms, can extract, classify and summarize textual data to provide actionable insights in no time.

To answer the question precisely, text analytics processes can save time, energy, money, errors and can increase productivity/efficiency of the business.

By implementing text analytics processes in your business, you can handle the data, convert it into structured formats, utilize the data to enhance your business and thereby gain a faster ROI.

You might want to read on: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

Below are the most important functions of text analytics processes.

1. Language identification: The most basic function of a text analytics solution is to identify the language of the text data. Once the language is identified, the next steps of text analytics can happen.
2. Tokenization: Tokenization is a process of breaking down the data/text into words, sentences, phrases, hyperlinks, punctuations, etc…
3. Sentence breaking: This function deals with the breaking down of a paragraph or text data into individual sentences by identifying the punctuations and tense of the data according to the language identified.
4. Chunking: Chunking is a higher level of sentence breaking that involves the person of speech and part of speech.
5. Syntax phrasing: Syntax phrasing is another sub-function of text analytics, where the sentence is broken down into words that have a negative/positive sentiment. This function of text analytics is the base for the sentiment analysis.

Also read: Text Analytics Strategy: 5 Steps to be followed by Enterprises

Text analytics has a myriad of applications/use cases in every sector. Be it the BFSI, healthcare, real estate, Retail & Ecommerce, logistics, manufacturing, or even the IT sector, text analytics can enhance businesses in a way that can save time, money and energy. Implementing text analytics software in your business can help the decision makers of the business take calculated and forecasted decisions related to the business as per the consumer`s preferences, behaviour and experience.

Text analytics helps enterprises improvise their business starting from insights on customer service till customer experience management. Relevant information based on the customer`s perspectives can be generated by implementing text analytics solutions to your business. Another major advantage of implementing text analytics processes is that it reduces the time of manual text analytics by 80%. The cost incurred during the entire process of text analytics is 1/4^th the cost of manual analysis. Text analytics solutions can help enhance businesses by providing sentiments, summaries, reports, and other such structured data from the already existing and incoming unstructured data.

The most trending use case or application of text analytics is the reviews summarization that can help understand the voice of customers of the business. When the sentiment and voice of the customer is reduced to a summarized form, the decision making becomes very simple yet highly efficient.