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Table of Contents ● What are Artificial Intelligence and Machine Learning? ● How AI and ML supercharge Text Analytics and NLP ● How do Machine Learning Algorithms work in AI Text Analytics?
How AI, Machine Learning And Text Analytics Business Intelligence www.repustate.com
In the era of big data how does a business harness the insights that lie deep within their enterprise data? One of the main ways is to apply artificial intelligence (AI) to text analytics. AI can help you automate the discovery of the most important insights that are hidden in your enterprise data. It can classify it to make it easy to analyze and apply to real world business problems. Specifically, AI text analytics can be used to make the analysis of text data faster, more accurate, and easier to scale. These data insights can be used to drive effective strategic change in operations, marketing, and sales to improve performance and competitive advantage. This information can also be coupled with predictive text analytics to extrapolate on past and present data patterns to help forecast future trending.
What are Artificial Intelligence and Machine Learning? Artificial intelligence can be best understood by elucidating the meaning of its words - “Artificial” being something made by humans rather than emerging naturally, while “Intelligence” is the ability to learn and apply knowledge. “Knowledge” is information or skills acquired by experience or training. Simply put, Artificial Intelligence, or AI, refers to machines that aspire to copy the basic characteristics of naturally occuring intelligence.
How AI and ML supercharge Text Analytics and NLP
Imagine someone asked you to read a page of any book and then tell them in 10 words or less what the page was about. You would probably quickly read the text and try to pick out the most important points and then use them to provide back a brief summary. This is what machine learning when applied to text analytics does, but much faster, more accurately and for millions of documents within seconds. Text analytics is the automated process of extracting the most high value, relevant information from documents and then summarizing them in a way that makes them visual, easy to analyse and much simpler to apply to remedy business challenges. AI supercharges the automated capabilities of computers to read and understand text by utilizing machine learning technologies like ML algorithms, NLP text mining, neural networks, and knowledge graphs.
How Is a VoC Survey Different From Regular Surveys?
A regular customer survey helps businesses assess the satisfaction level of previous customers but a VoC survey takes analysis to the next level. A VoC survey helps understand customer pain points across the entire sales journey and discovers deeper aspects that are not possible with regular surveys. It’s no secret that customer service is integral to business success. Around 84% of consumers admit that customer service is a key factor in decision-making. Even so, during difficult times, expectations seem to be higher. So having insights from a VoC survey gives you an edge to always be ahead. A Voice of the Customer survey is a fundamental part of the Voice of the Customer process. The process involves understanding the customer needs and how the customer experience (CX) can be improved. It follows the entire customer journey, uncovering various pain points or obstacles along the way. Each of these discoveries can help improve the overall level of customer satisfaction.
How do Machine Learning Algorithms work in AI Text Analytics? Aspect Based Sentiment Analysis - A question that many companies ask is how do I apply AI or machine learning to help me drive my text or predictive analytics? The answer is quite simple. All AI/ML processes follow the same pattern: 1.
Collect data
2.
Clean it up
3.
Train
4.
Test
5.
Go back to 2/3 if results are not good enough
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