Differences Between Web Mining and Text Mining

Differences Between Web Mining and Text Mining

There are differences between web mining and text mining due to the different kinds of data being mined. Structured data is the text that can be neatly fitted onto a spreadsheet and be conveniently searched and analyzed. Text mining looks at this kind of data by imposing this structure. But Web data mining’s contents are the other forms. These are considered unstructured due to the varied free form styles in which they are found, such as a Word document or a graph.

What Is Web Mining?

Web mining is a process which includes various data mining techniques to extract knowledge from web content, structure, and usage. It can be used for discovering useful information previously unknown.

Web mining can be classified based on the following categories: 

  • Web Content 
  • Web Structure 
  • Web usage

What Is Text Mining?

The process of text mining is the transformation and interpretation (often mathematical) of unstructured texts into structured data for purposes such as identification patterns. The idea behind text mining is to find patterns and associations in documents, which can be used for a variety of purposes.

Text mining, or text analysis, is an important research field that has applications in many fields, such as information retrieval and lexical decomposition. The goal of text analytics tools like these are to provide insight into the meaning behind word frequencies.

Web Mining Technologies

  • Web Content Mining Web content mining is the process of converting raw data into useful information using the content on web pages from a specified website.
  • Web Structure Mining The web graph is a structure that consists of nodes and hyperlinks. The presence of these connections between pages makes up for an edge. Document-level analysis looks at the links between pages within a single document while hyperlink analysis assesses relationships among different documents on an Internet web page or web site.
  • Web Usage Mining The Web is a collection of interrelated files housed on one or more servers. Leveraging the client-server transactions, patterns of meaningful data are discovered.

Text Mining Technologies

  • Summarization Summarizing a large amount of data while maintaining the main idea.
  • Information Extraction Using pattern matching formats to extract information. 
  • Categorization Supervised learning technique that categorizes the document according to content.
  • Visualization Using computer graphics to represent information and to visualize relationships.
  • Clustering Grouping according to textual similarity based on the unsupervised technique.
  • Question Answering Using a list of patterns to answer a natural language question.
  • Sentiment Analysis Also known as opinion mining, it gathers peoples’ moods about a service or product. 

Differences

The basic difference between web mining and text mining arises from the difference between two natures of the data. 

  • Text mining imposes a structure to the specified data to be mined for valuable information. 
  • Web mining deals with the unstructured forms of data, which includes Word documents, PDF files, and XML files.
Conclusion

Most data found in the world is unstructured. But many businesses are data driven. They depend on analyzed data for valuable information to make business decisions that generate growth and revenue. Natural Language Processing (NLP) is a powerful tool that can be used to create accurate and complete taxonomies, which will help in the metadata association process.

Plain Language In Business

Plain Language In Business

Plain language means writing with clarity for the reader. It means removing any content which could detract from your message or the intent of what you are trying to convey. In business, plain language is important for a successful business. Drawing the interest of customers or other business partners will fail if the language spoken is not clear.

Why Plain Language In Business Is Important

Plain language is not boring, and it is not about “dumbing down” information either. It is a matter of making what would be complex or confusing for those reading understandable with ease. It is accomplished by using simple words in a straightforward, expressive way so there are no barriers preventing communication.

If you want to get your point across, it’s important for the audience members to understand what they are hearing. If a language or writing style is not understandable by everyone in an organization then communication has failed and will never succeed.

With the increasing need for English reading comprehension today, it is unfortunate that many organizations are producing documents that require above-average skills. This puts an unfair expectation on their audiences. Not only is it bad business sense but it will discourage their customers who do not understand what is being said.

Plain Language In Business Impacts Time

Plain language in business is directly related to time. No one has time for long emails. It is not that they cannot read them, it is just an unnecessary burden when there is so much else going on in peoples’ lives.

If you want your message to be heard, don’t speak in a way that is hard to understand. Spelling and grammar are important for all forms of communication. They also matter in business emails or other messages where clarity is key to getting an audience’s attention about products or services.

Plain language will ensure faster business transactions.

Plain Language In Business Impacts Money

Communicating in a way that is understandable to others requires the speaker to use words with meaning. When someone does not understand your foreign language, they will likely only hear sound waves instead of understanding what you are trying to say.

40% of the total cost for managing business transactions is spent on problems resulting from poor or misunderstood communication. Such corporate monetary waste can be eliminated simply by adopting plain language communications.

Conclusion

Corporations should be clear when drafting contracts to procure business. Contractual language should be plain and understandable. Without such clarity in the language, both sides stand to lose something important. Promises might fall short of desired requirements. Expected obligations might not be met. And there will be violated trust and damaged business prospects.

Advanced software technologies using AI and NLP can help companies to know whether their contract language is plain enough for fair dealings. It can demonstrate whether the contractual language is readable or not. The Content Analytics Platform (CAP), developed by Scion Analytics, can provide readability scores for any size document, regardless of its complexity.

NLP In Finance Examples

NLP In Finance Examples

Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), is being used in the financial trades increasingly. The financial trades are benefiting in the areas of risk assessment, financial sentiment, accounting and auditing, and portfolio selection and optimization.

NLP performs analyzing, understanding and then generating human-like language for interfacing. The software-generated language can be produced in both written and spoken contexts. NLP enables computers to comprehend human language and to respond with human language.

The quest to make computers understand and use natural language is an interdisciplinary area, drawing on various fields including linguistics. In recent years there has been progress made by several computer scientists who have begun incorporating what we know about human cognition into AI systems so they can more effectively detect insights from large quantities of data. With the advent of machine learning algorithms and increased computational abilities, NLP has become much more scalable.

Financial Risk Assessment

Banks can quantify the chances of a successful loan payment based on credit risk assessment. Usually, this is calculated using past spending patterns and data about previous payments to estimate what consumers are likely going to pay back their loans. Personal loan documents are usually full of information that can be used to help companies determine a person’s credit risk.

NLP is a practical and effective way to assess credit risk. For instance, NLP can measure risk assessment by looking at the borrowers’ subtle emotions during the loan process. Similarly, it can also point out incoherent data and take it up for more scrutiny.

Financial Sentiment

Successful trading in the stock market depends on information about select stocks. Based on this knowledge, traders can decide whether to buy or sell a particular security at any time and place that works best for them!

It is crucial for an investor to be aware of what analysts are saying about companies, and this can be found on social media.

Financial sentiment analysis is different from routine sentiment. In financial news, the objective of this type of research and reporting on stocks is to see if there will be an increase in prices or decrease once people hear about it.

Financial Accounting And Auditing

Traditionally, financing institutions used to deal with endless daily transactions and invoice-like papers for many years. They now realize the important help that NLP gives to these processes. NLP enables financial professionals to detect anomalies or irregularities, in the day-to-day transactions. Using the right technology, less time is needed to notice anomalous transactions and their causes. NLP can help you identify significant potential risks and possible fraud, like money laundering.

Financial Portfolio Selection And Optimization

The goal of investors is to increase its capital over time. Using the collected past data, NLP can help predict the trade period and portfolio. And using this data, the investors can smartly distribute their capital among the assets currently available.

Stock Behavior Predictions

NLP combined with deep learning can help enable predictions for time series for financial analysis. These combined techniques are useful for predicting the volatile prices of the stock market. They are also valuable for determining stock trading decisions.

Natural Language Processing Examples

Natural Language Processing Examples

Natural Language Processing (NLP) deals with the use of Machine Learning (ML) to understand human languages. This advanced technology has been around for years, but people have not realized it because consumers are using this technology every day. 

NLP is a branch of artificial intelligence (AI). It performs analyzing, understanding and then generating human-like language for interfacing.

Artificial Intelligence (AI) is an advanced level of digital automation where software mimics human intelligence and judgment.

Machine Learning (ML) is a part of Artificial Intelligence (AI). ML is the ability of computers to learn by using algorithms, which are like digital flowcharts using rules.

Here are some examples of the use of Natural Language Processing (NLP) across a wide range of industries:

Social Media Monitoring

As many marketers and business owners know, having a negative review go viral on social media can destroy your brand’s reputation overnight. NLP can help with social media monitoring.

Sentiment Analysis

NLP and sentiment analysis tools are used to identify emotionally charged words that make up customer reviews. These can be used as an insight into what a specific reviewer is feeling about a brand.

Text Analysis

Text analytics can be a powerful tool for companies to organize their data and identify useful patterns and actionable insights.

Survey Analytics

Companies can also use their survey results to come up with actionable insights.

Spam Filters

Spam filters use NLP technology to analyze email subject lines and their body content. This is done by looking at various factors, such as overuse of capitalized letters, misspellings, strange or inconsistent URLs, and offers which are too easy or unbelievable, etc.

Email Classification

Gmail mines emails for keywords and phrases, then uses NLP to evaluate their content. Gmail identifies which ones are worth reading by categorizing them into categories like “social” or “business.”

Autocomplete

Google’s autocomplete feature will predict what you are interested in searching for, based on the initial few words that are typed or the characters.

Autocorrect

NLP can automatically correct your spelling errors and generate the closest possible word.

Spell Check

Spell check is a valuable tool that can help you avoid making mistakes.

Smart Search

Web pages that contain the search function can help you find exactly what you are looking for if there are relevant results.

Chatbots

Chatbots can take on several different roles, from recommending products and services to making appointments for you. They are even getting better at understanding what people want. The use of NLP with ML means that chatbots’ output has improved significantly.

Smart Home Devices

One of the most popular home technologies in recent years is Alexa. This speaker can be used to play music, control lights and other devices around your house, as well as read news headlines or weather forecasts aloud on demand. A similar device called Google Home has also been gaining more attention, thanks to its ability not only to answer questions but make reservations at restaurants. 

Conclusion

Advanced technologies employing the AI tools of NLP and ML have proven to be useful in improving the ease and efficiency of modern living everywhere. The Content Analytics Platform (CAP), developed by Scion Analytics, also uses Natural Language Processing (NLP) that can help businesses gain actionable insights with their own data analytics. 

NLP vs NLU vs NLG

NLP vs NLU vs NLG

Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are closely related topics but are distinct from one another. These are related because they deal with the human language. These are all natural language topics because they are addressing how humans naturally speak with one another rather than how a computer may speak to another computer. All three components play a part just as they do in the human experience of conversation. In our normal conversation, we not only speak words in a certain way, but we also try to understand other persons’ words as we listen to them. Further, when we answer, we are generating speech for our response.

What Is NLP?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). Besides AI, it is a product of disciplines such as computer science, linguistics, and data science. These are used to enable computers to understand human language as written or spoken. NLP especially uses the technologies of Machine Learning (ML) and deep learning to be enabled to accomplish such things as language translations or answering questions.

NLP processes the unstructured data of human language, spoken or written, by restructuring it into digital data more easily understandable to intelligent computer programs. This is done by detecting named entities and word patterns and looking at the root forms of words.

What Is NLU?

Natural Language Understanding (NLU) is a subset of NLP. It is employed by NLP to perform the part of understanding the language that NLP is processing. For example, our general thinking is like the NLP part, and our understanding of what we hear or read is like the NLU part of the process.

Without getting too deep, this NLU part does an analysis of semantics and syntaxes of text and speech. It tries to discern the meaning of what is received. Analyzing the syntax of a sentence helps us understand the grammatical structure: what is being said. Think of the phrase, “Are you asking me or telling me?” Analyzing the semantics of a sentence helps us understand the intended sense: what is being implied. Think of the phrase, “What are you implying by that question?”

What Is NLG?

Natural Language Generation (NLG) is another subset of NLP. It does the function of providing output language in response to the input language. It does the answering in written language understandable to humans. This response can also be converted to spoken words by using text-to-speech services. Originally, the NLG component simply performed a method of filling in blanks according to a template in response to key phrases or questions. But NLG has evolved into a more dynamic process by using such services as recurrent neural networks, transformers, and hidden Markov chains. A hidden Markov model is a statistical method to guess what will be said based on some other known parameters.

Conclusion

NLP and the companion components of NLU and NLG are a group of significant Artificial Intelligence (AI) software technologies. They have revolutionized the way digital technology serves human intelligence. These technologies enable computers to interact with people in a much more human-like way. They make the services that computers provide much more accessible for the greatest number of average users possible.

Benefits Of Automation in IT

Benefits Of Automation in IT

Automation in Information Technology (IT) means the automating of data calculations, algorithms, and processes required for digital technology needs, which in turn serves other needs required in various industries. In a sense, to speak about automation in IT is to speak redundantly because the very principle of information technology is automation itself. With the clever manipulation of a binary system, calculations and processes are automated with inhuman efficiency and speed. This is because the underlying actions of calculations, arrangements, discoveries, and other processes are themselves first being done with amazing ease, speed, and reliability. These first actions are the results deriving from the technologies of Natural Language Processing (NLP) and Artificial Intelligence (AI). Common business tasks can be accomplished with the largest parts being automatically performed.

Automated Reasoning Technology

Every high-tech industry has products or services that are suited for human consumption. These commodities are sensible for our existence. When qualified people provide these products or services in some small measure, without the advantage of IT assisting the many intermediate processes, then the overall output of the accomplished work may be of good quality but will inevitably be very limited. But the abilities of IT use such basic tools as calculation, computation, arranging, and sorting. These IT tools increase output exponentially. All these abilities are done by underlying human reasoning design. Natural Language Processing (NLP) and Artificial Intelligence (AI) are two other technological advantages that help with data management needs by the associative reasoning. These learn, in a sense, of what people are saying online, and then leveraging that data for business and other purposes.

Automated Document Data Management

Important business documents that need to be perused or analyzed, such as proposals, contracts, and resumes, require much time and care when being done by individuals themselves. This method will be slow and possibly inaccurate. The larger the documents, such as request for proposals (RFPs) and business proposals, legal contracts and government bills, the more cumbersome to be parsed. They demand many personnel and manhours of labor to accurately absorb and analyze the data content for successful response or manipulation. But imagine what you could do if you had a single application that could comprehensively, accurately, and speedily analyze content data automatically with a few keystrokes? Such a software tool would effectively demonstrate the key benefit of automation in IT.

Conclusion

There are many benefits of automation within Information Technology (IT) itself. With IT, we have the assistance of efficient robots in the workforce, which in turn provides the many benefits of accurate products, economic use of resources, tireless output, a safer workplace for personnel, lower overhead costs, and affordable products and services for the consumers. The remarkable benefit of automation provided by, and due to, IT itself is what the Content Analytics Platform (CAP), developed by Scion Analytics, is about. The application uses such key technologies as Natural Language Processing (NLP) and Artificial Intelligence (AI) to quickly analyze document data, and structures that data so that it can be leveraged for successful business accomplishments

Benefits Of Sentiment Analysis

Benefits Of Sentiment Analysis

What Is Sentiment Analysis

Sentiment analysis, also known as opinion mining or emotional AI, is a way of knowing the thoughts or feelings of people regarding any topic expressed on the Internet. It means analyzing people’s online textual comments regarding a certain matter, service, or product to gather the essential mood they feel. Sentiment analysis uses the digital technologies of Natural Language Processing (NLP) or Artificial Intelligence (AI). This process is beneficial especially for various business products and services but can serve any area of life. In business, it is valuable for marketing, e-commerce, advertising. It can also be useful in politics or any global research need.

Benefit For Business Information

Perhaps the primary use for sentiment analysis is providing businesses the ability to be aware of how their products or services are appreciated by the consumers. This lets companies know how to improve or limit their offerings. People naturally want to see that their efforts at helping in some way is useful. Sentiment analysis is a digital way for a company to assess this.

Source For Customer Feedback

Every high-quality business cares what consumers think about their purchased products or services. Sentiment analysis can gather these publicly expressed attitudes through the canvassing of social media, blogs, articles, reviews, and discussion forums. Using the technologies of NLP and AI, general customer feedback can be known.

Monitor For Market Buzz

Vast amounts of online textual data is readily available for sentiment analysis to help see market interests or trends regarding products or services. These can be new or old, niche or ordinary. In a sense, online textual data is like scattered commodities that just need to be picked up by the savvy marketer. When the public express what they like or dislike, then a company can know what will be successful or not.

Means For Crisis Prevention

Most companies care about their brand’s quality. When consumers are dissatisfied with a product or service, they feel compelled to mention these opinions through social media, websites, or product reviews. These sentiments can be detected in real-time to prevent large-scale damaging impressions. If monitored well, it also helps with real-time customer service in the effort to prevent widespread public disapproval. Not only in the marketplace but also in national or global political affairs can sentiment analysis be useful for preventing bad public persona or threats of violence.

Conclusion

Sentiment analysis is becoming important in the business of marketing, e-commerce, advertising, and even politics, because it is a process of analyzing the opinions and attitudes of people anywhere technology is available. Things which would not be possible without the digital technologies of NLP and AI, which uses algorithms and rules to learn how people speak and express their opinions about anything posted through any digital medium, such as social media, websites, blogs, and discussion forums. The Content Analytics Platform (CAP), developed by Scion Analytics, is a robust software application that uses these technological advances for companies to leverage invaluable structured data.

How Sentiment Analysis Can Analyze Mood

How Sentiment Analysis Can Analyze Mood

COVID-19 And Sentiment Analysis 

2020 was a year that changed history with the spread of the COVID-19 pandemic. As the world shut down and people congregated online, social media channels lit up with collective moods and feelings about the pandemic. Historically, such data would have been subjective however with technological advances in text mining, it was not. Sentiment analysis has evolved in recent years with more uses in the business and public sector.

For the past 50 years, researchers have been working on technology that would enable computers to analyze the emotional tone of words. Such findings have had many real-world applications. For example, a large collection of tweets can be analyzed for collective mood something that would be impossible given human limitations. Now, music researchers can quantify how much a minor chord is sadder than a major chord. And businesses are tapping into websites like Yelp to understand customer behavior better. By assessing a vast number of reviews businesses can align brands with what customers feel and want. Sentiment analysis has medical applications as well with popular platforms like Facebook being able to recognize if users are depressed or suicidal.

When algorithms can gauge mood from what people write online it opens a new world of data and opportunity for society.

How Sentiment Analysis Works

How does sentiment analysis work? Advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) have mastered analyzing the context of language. However, AI has yet to understand the nuances of language. It was a significant win for science that artificial cognition can decipher the emotions behind words without understanding them.

In the beginning, sentiment analysis used word-counting for analysis. It added up the number of positive words and subtracted the negative words. Some content analytics platforms use weighted analysis to determine the mood of the content. For example, the word “atrocious” is given a higher weight than the word “bad”. Humans assign weights to words by creating

customized dictionaries within the software. In the sphere of sentiment analysis, these dictionaries are called lexicons.

However, human work is prone to error. Word-counting is fundamentally effective but ultimately unreliable. There are too many variables in language for word-counting to be precise. A more sophisticated approach to sentiment analysis uses Machine Learning (ML) algorithms to teach computers to recognize patterns and relationships between words. For example, a computer can recognize that “computer” and “mouse” are related and that it is a different type of “mouse” than the rodent variety.

Sentiment Analysis And Mental Health

Computer scientists are working on further advancements in sentiment analysis which has a foundation in psychology. As early as the 1960s, psychologists in leading universities were designing programs that were giving insights into individual’s psychological worlds. For example, computers were able to pick up on specific patterns for patients diagnosed with depression. Depressed patients were apt to use pronouns more often such as “I” and “me” as well as negative affect words or words related to death. These insights have empowered social media platforms to be proactive about user’s mental health. For example, Facebook has an algorithm that can identify suicidal users and if necessary, send the user helpline numbers.

With roots in psychology, sentiment analysis can give tremendous insight into the collective mood on social media. This has implications in the lives of the consumer and the individual as businesses become better at understanding consumer behavior. Also, individuals become engaged in social media platforms that can accurately decipher their mental health. It represents a shift in how much influence collective mood has on society.

Text Mining vs. Natural Language Processing

Text Mining vs. Natural Language Processing

In the 21st century data has become as much of a commodity as oil was in the 20th century. As businesses become more reliant on data-driven decisions, the importance of utilizing the right type of data methods to process the right information for businesses becomes paramount. Access to unstructured data has emphasized the need to leverage large data sets. Businesses are no longer able to process large sets of textual data manually and have come to rely on automation. To learn how to best extract value from data, it is important to understand the difference in benefits of Natural Language Processing (NLP) versus text mining.

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Using Natural Language Generation (NLG) and Natural Language Understanding (NLU), NLP can process different types of speech including misspellings. It does that by utilizing Machine Learning (ML), a system that relies on special types of databases to extract language.

Text mining is a subtype of data mining. It focuses on data mining and ML methods as it relates to textual information. More specifically, it extracts the information from text files. A wide array of text files can be used in text mining including structured and unstructured data in emails, social media posts, and web content. Text mining works for qualitative data analysis and it helps distinguish between different types of data.

Differences Between Text Mining v. Natural Language Processing

1. Purpose

NLP and text mining differ in the goal for which they are used. 

NLP is used to understand human language by analyzing text, speech, or grammatical syntax. 

Text mining is used to extract information from unstructured and structured content. It focuses on structure rather than the meaning of content.

2. Tools

Businesses use different tools when working with NLP and text mining. 

NLP uses businesses leverage advanced ML models, artificial neural networks (ANN), and tools like NLTK in Python. 

Text mining also uses ML models along with statistical models and text processing languages like Perl. A business must invest in the tools necessary for specific data processing to be successful.

3. Capability

NLP is geared towards mimicking natural human communication. It uses text and speech as input to extract grammatical structure and syntax meaning. 

Text Mining is geared towards analyzing qualitative data.

4. Results

 The outcomes for using different data methods differ. 

NLP can be used to extract grammatical structure as well as the sentiment from language. 

Text mining uses statistical indicators like frequency of words, patterns of words, and correlation within words to explain the text.

Differences Between Text Mining v. Natural Language Examples

The major difference between NLP and text mining is in potential application.

Natural Language Processing

In everyday use, NLP can be found in search engines that provide correct answers when users enter queries. It can also be seen in intelligent chatbots that are integrated into communication channels and websites to provide customer service. Another application of NLP is spellchecking apps, tools like Grammarly, have huge databases of words, grammatical rules, and combinations that are powered by NLP.

Text Mining

Text mining is used for SEO and website marketing purposes. It can guide contextual advertising and target promotions. Another application of text mining is analyzing data from website content and social media platforms.

Conclusion

While NLP and text mining have core differences in application, they both provide advantages when used to analyze data for a business. These data methods save time and resources, are more efficient than human intelligence, and track information flow. Gathering insights from structured and unstructured data, NLP and text mining provide valuable insights that move businesses into the future.

Natural Language Processing (NLP) Consulting

Natural Language Processing (NLP) Consulting

What Is Natural Language Processing

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

NLP In Business

As technology advances and humans leverage data for everyday life, the field of NLP becomes more sophisticated. NLP is used to improve the quality of life for humans. It is being integrated into daily lives with intelligent virtual assistants such as Siri and Alexa. These virtual assistants are responsive to human needs and become more intelligent with every interaction with a human. Soon, intelligent automation will enhance human intelligence enabling humans to pursue higher value-added projects requiring ingenuity and creativity.

Businesses can tap into data they did not even know they had to make actionable data-driven decisions. Enterprises benefit from insights, analytics, and decision-making from analyzing 80-90% of hidden data. Unstructured data has many formats from text, voice, audio, images, and videos that can be processed to yield new marketing, sales, and operations initiatives for businesses. By extracting value from unstructured data, a business learns more about its customers and their preferences. For example, a lot of social media channels are full of unstructured data where NLP can be applied to do sentiment analysis. Conducting sentiment analysis on social media, a business gains insights into customer mood and feelings over a product or service. Now, the decisions the business makes surrounding that product or service are not based on guesswork but data.

NLP Consulting

At Scion Analytics, we focus on delivering AI & NLP consulting services with the Content Analytics Platform (CAP). The CAP has NLP capabilities to liberate value from unstructured data that automate processes, scale business, and leverage data to transform the enterprise. By using the CAP, the enterprise can automatically extract value out of content to reduce the sales cycle. It can also use NLP technology to understand the contextual relationship between large data sets which enable the enterprise to execute on opportunities. By leveraging real-time analytics, the enterprise can capitalize on data-driven decisions much faster and gain a competitive advantage. The CAP uses AI & NLP technologies to analyze and process unstructured data by configuring customized microservices for clients. Scion Analytics use cases have found that each time a client uses 1 microservice for every use it saves the client 8 hours of time. For example, a microservice designed for business requirements could parse business documents for predefined keywords and create a dictionary. Therefore, a process that would take hours manually for an employee to go through business documents is automated in minutes.

For the enterprise, the benefits of using a content analytics platform are saving time, increasing revenues, and efficiency. It makes sense that a business that is shaping the future would embrace NLP technology and leverage NLP consulting services to succeed.