What is Text Analytics And How it is used?

The practice of analyzing unstructured and semi-structured text data for significant insights, trends, and patterns is known as text analysis. It is generally utilized when huge amounts of text-based data need to be processed for insights but it would otherwise be too resources and time-intensive for people to analyze manually. Text analysis might be utilized in the following situations:

• Analyzing 1,000 customer questionnaires for feedback on new product launches
• Examining the last three years of support tickets for recurring customer issues.
• Identifying the most important subjects to incorporate into your chatbot or knowledge base

Text analysis algorithms would surpass humans in all of these scenarios. Not only are the final insights less time and resource-intensive, but they are also more consistent, with fewer human mistakes or biases interfering with the process. As a result, text analysis approaches are being used by many organizations across various industries to get quantitative and qualitative knowledge of their text collections. These are then used with data visualization tools to improve the translation of information into actionable insights for informed decision-making. Boost your Skills by learning: Digital Marketing
 
Table of Content:
1) What is the process of text analysis?
2) Examples of Text Analytics
3) What distinguishes text analysis from natural language processing (NLP)?

What is the process of text analysis?
Text analysis is centered on teaching computer software to correlate words with certain meanings and to comprehend the semantic context of data that is unstructured. Linking words with objects, activities, and feelings is comparable to how people acquire a new language. Text analysis software is based on deep learning and natural language processing techniques:

• Natural language understanding: Natural language processing (NLP) is an area of artificial intelligence that enables computers to draw meaning automatically from natural, human-created text. It trains deep learning technologies to process and evaluate text data, including handwritten text pictures, using linguistic models and statistics. NLP technologies like optical character recognition (OCR) turn text pictures into text documents by locating and comprehending the words in the images.
• Deep understanding: The discipline of data science known as artificial intelligence encourages computers to think like humans. Machine learning is an artificial intelligence technology that employs specialized methodologies to instruct or train machines. Deep learning is a highly sophisticated machine learning technology that employs neural networks or software structures that are similar to those found in the human brain. Text analysis software is powered by deep learning technology, which allows these networks to interpret text in a manner comparable to the human brain.

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 Examples of Text Analytics:
This involves assessing product and service reviews to discover how your consumers rate your firm, processing open-ended customer survey replies, and monitoring what customers say about your brand on social media. Assume you wish to evaluate NPS open-ended replies to see which components of your business are being addressed by your consumers. The first step would be to do a topic analysis, which is a text analysis approach that automatically identifies NPS replies based on predetermined categories like Feature Request and Customer Service. Here are a couple such examples:  
"Customer support is really friendly and effective!" Customer Support
After you've categorized each NPS answer, you can utilize text analytics to identify trends and insights throughout the whole dataset, and then generate charts or reports to show the results. The graph below demonstrates how Retently utilized text analytics to map common topics in NPS responses:
The graph demonstrates that the majority of negative comments are about Product Features, while the majority of good comments are about Product UX and Customer Support. The negative feedback indicates that fulfilling popular feature requests might help Retently improve customer satisfaction. Aside from support tickets, text analytics may be used to gain crucial data from other customer service channels such as email, chat, and social media. For example, we examined customer support interactions on Twitter for telecommunications firms and discovered that T-Mobile had the most favorable conversations.
Sprint, on the other hand, has the most unfavorable Twitter mentions: Detecting a surge in customer service complaints in a timely manner, for example, may suggest that you need to give additional training to your agents, enhance the routing procedure for support issues, or make your help center more user-friendly.
 
What distinguishes text analysis from natural language processing (NLP)?

• Natural language processing is essentially a subset of the larger area of text analysis, which is why the distinction between the two can be difficult to understand.
• Let us first define text analysis and natural language processing.
• Text analysis is the process of studying vast amounts of text in order to discover new and relevant insights.
• Natural language processing (NLP), or more particularly, natural language understanding (NLU), enables machines to "read", "understand", and mimic human speech.
• Natural language processing is one of the analytical approaches utilized in the text analysis process to gain insights. NLP is a multidisciplinary area that includes text analysis, computational linguistics, AI, and machine learning.
• The primary distinction between text analysis and NLP is found in their respective purposes.

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• Text analysis seeks to generate excellent insights purely from the text or words themselves. The text's semantics are ignored. It provides responses to queries such as word frequency, sentence length, and the presence or absence of words.
• NLP, on the other hand, seeks to comprehend the linguistic usage and context of the text. Grammatical structures and semantics are examined here. It provides answers to issues such as the meaning of a sentence, people's linguistic tendencies, and even categorizes which of your emails should go into the Primary, Social, Promotions, or Updates sections.
• Both are frequently used in tandem to give a numerical and contextual understanding of human communications.
• So, how do they come into play in business? The next part will go over text analysis use cases and applications.

The practice of analyzing unstructured and semi-structured text data for significant insights, trends, and patterns is known as text analysis. It is generally utilized when huge amounts of text-based data need to be processed for insights but it would otherwise be too resources and time-intensive for people to analyze manually. 

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