What makes an industry successful? The ability and the willingness to innovate with the times and customer demands to deliver better services and products to the customers. Finance is one of those industries. Highly regulated and complex, finance has leaned into new technologies to deliver a better customer experience and embrace opportunities. Text analytics technologies have been a natural fit for finance as unstructured data became accessible to the industry.
These days, the financial sector can derive high-quality structured data from unstructured text. Unstructured data from sources such as social media, email, instant messaging, and online forums are being used for data-driven insights. Processes like sentiment analysis are being leveraged for financial applications. The predictive power of sentiment scores is a game changer for financial professionals.
What Is Text Analytics
Text analytics is the process of converting large sets of unstructured text into quantitative data. Finance professionals and data scientists can mine unstructured data to uncover insights, trends, and patterns. This enables businesses to compile a story behind the numbers to make more precise decisions.
Unstructured data is the new currency for the digital age. Businesses had leveraged structured data for years. Data that was organized and easy to mine provided limited insight into what made customers tick. The opportunity to mine unstructured data changed all that. In an organization, 80-90% of data is unstructured. Though, free form and difficult to access, unstructured data provides a wealth of data to fuel text analytics tools.
Text Analytics Examples
The financial sector industry is ahead of the trend when it comes to utilizing text analytics to gain insight. Here are some examples:
Good investment practices can be a profitable venture in finance. Investors seeking a more data-driven approach lean on text analytics for insights.
2. Portfolio Managers
Use text analytics to cut through the noise to recognize important highlights in investor notes, blogs, and news.
3. Data Scientists
Fuel trading models and investment strategies with structured data that has been derived from unstructured text content.
4. Compliance And Risk Managers
Leverage text analytics tools to increase compliance rates and get alerted to relevant internal conversations.
Benefits Of Text Analytics For Finance
The financial sector is a $35.5 billion market that invests heavily in the information industry. Finance accounts for 9.8% of the entire information industry. The investment pays off as text analytics tools yield tremendous benefits for financial professionals. Text analytics practices such as sentiment analysis take client’s everyday content such as emails, social media, and personal information convert it into forward-looking insights. For the finance industry, text analytics provides the opportunity to hyper-personalize the customer experience and improve the bottom line.
Unstructured data has redefined the way businesses process information. It has been the catalyst for innovation for many industries including financial services. From investors to compliance and risk managers, text analytics tools such as the Content Analytics Platform (CAP) from Scion Analytics enable a more successful and personal customer experience.
Businesses use textual analysis to mine vast sets of data with minimum human effort. For businesses, investing in textual analysis tools saves time and resources allowing the focus to shift to more value-driven tasks. Most of the new data is created in an unstructured format. Traditionally, data has been stored in a pre-defined structured format for ease of access and analysis. Unstructured data presents a challenge for analysis. It is in free form requiring specialized tools to extract meaningful insights.
Today, it is hard to estimate how much digital data is on the internet. The meteoric rise of data can be attributed to the last two years when 90% of all the data in the world was generated. From social media messages to emails and Google searches, the sources of online data keep multiplying from people spending more time online.
Advantages Of Textual Analysis
For a business, using a textual analysis tool such as the Content Analytics Platform (CAP) has several advantages:
A textual analysis tool provides instant analysis and results. You can save time and make teams more productive by automating the process of textual analysis and then repeating it.
2. Real-Time Analysis
These days the world operates faster than ever. Accurate textual analysis can help a business acknowledge dissatisfied customers on time or handle a PR crisis. This is done by real-time monitoring of reviews, chat, social media channels, and customer feedback. Using real-time insights prepares a business to take the action needed before a crisis or problems unfold.
3. Consistent Results
Manual work is prone to errors and inconsistencies. In a business, routine tasks such as replying to customer feedback can become repetitive and time-consuming. Textual analysis can alleviate tedious tasks with consistent analysis of data.
Why Does Textual Analysis Matter?
Textual analysis can be used in a variety of industries with impactful results, such as:
– Sentiment analysis for social media to understand a psychological and emotional state of a person active on social media
– Make distinctions in different contexts and forms of communication
– Recognizing a pattern of communication in a target audience
– Detecting bias or incendiary speech in communication
– Analyzing real-time communication and social interaction
Textual analysis can be applied to any piece of writing in a variety of fields. From social sciences, psychology, political science, marketing, literature, and sociology, textual analysis can be used to analyze the context, audience, and purpose behind communication. This enables industries to spot trends and anticipate the needs of the audience. It enables businesses to develop product lines, interact with customers, and scale the business while being responsive to customer feedback and attitudes.
Textual analysis is just part of the new wave of tools being developed to transform the way content is consumed and communicated. It gives insights that shape Artificial Intelligence (AI) practices for the development of more accurate machine algorithms. It also takes the tremendous amount of data available on the internet and tries to make sense of it for a business in real-time.
Welcome to the Internet. A stratosphere of content that is humming, exploding, and lighting up with content every minute. As of 2020, 78 million WordPress posts are going up every minute, 4.4 million Google searches a minute, and 350 million Tweets every minute. That is a lot of content to generate data for text analytics for marketing nerds everywhere.
Any experienced content creator knows that there is a difference between pushing out content to an audience and an audience engaging with content. The stratosphere is full of noise, chatter, waste but what about the content that influences, trends, and even goes viral?
The Nun Study
Text analytics can slice and dice data to uncover insights about consumer behavior. But science has a say in content creation as well. In 1990, the University of Minnesota did a “Nun Study” to look at the onset of Alzheimer’s. The population chosen for the study were nuns from the School Sisters of Notre Dame. A sample of 678 nuns from the congregation was chosen to have their brains examined post-mortem.
The findings were interesting.
The study looked at the “linguistic density” of essays written by the nuns at the age of 22. The evaluation of “linguistic density” could predict with 80-90% accuracy whether the nuns would develop Alzheimer’s later in life.
What is Linguistic Density?
A simple way to understand linguistic density is that it represents the number of ideas in a sentence divided by the number of words in a sentence. For example,
The girl’s cotton candy is pink.
This sentence has 3 ideas: The girl has candy.
The candy is cotton candy.
The color of the cotton candy is pink.
3 (Ideas)/6(Number of words)= 0.5 language density
Therefore, linguistic density is correlated with the complexity of language. Young people who write with a high linguistic density have lower instances of neurodegenerative diseases in old age.
Linguistic Density Of Writers
This principle can be applied to published writers as well. The University of Toronto did a study on the works of famed British author Agatha Christie. Christie was a prolific author throughout her life until the later years when her writing had become erratic and vague.
The research examined her book, “Elephants Can Remember” in which the main character cannot solve a crime because of her faltering memory. In that book, the size of Christie’s vocabulary decreases, she became repetitive, and the complexity of her words diminished significantly. Later in life, Christie became reclusive, and many fans hypothesized that she succumbed to a neurodegenerative disease.
Real Life Creates Content
Why is language density important when considering content on the Internet? Scientific findings have shown that the command of complex language is better for cognitive functions. But what about the consumption of complex language? Content that is more language dense provides more value to the reader. It intellectually challenges the audience and encourages them to exercise their brain.
High language density in content is more engaging to the reader. It paints a picture, tells a story, and makes for good data for Text analytics. In the content stratosphere that is the internet, high language density content is a bright star.
Text mining is an Artificial Intelligence (AI) technology that uses Natural Language Processing (NLP) to gain insights from unstructured text.
According to research, less than 1% of the data in the world is analyzed and processed. As businesses learn how to leverage data, they did not even know they had, they experience a paradigm shift in the way business is done. By using text mining and text analysis, a business can gain quantitative insights by tapping into text analytics.
The vast amount of data generated every day represents a tremendous opportunity for businesses across the world. It can be used to gain granular insights into customer feedback on a product or service.
This can be achieved by the flow of data from:
– Product reviews
– Social media analytics
– Customer surveys
All these sources of data need to be processed to be actionable. That is where the practice of text mining comes in.
How Text Mining Works
Text Mining v. Text Analytics
To establish how text mining works, a distinction between text analytics v. text mining needs to be made. Text mining can be distinguished by the fact that it works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze customer feedback to determine if customers are dissatisfied with a product. Text analytics can gather deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. For example, text analytics can be used to interpret a decline in the popularity of a product over time.
Qualitative insights from text mining can be enhanced using NLP. For example, one of the common uses of NLP/text analytics is social media monitoring. Social media monitoring is done on a large sample of user-generated content to understand behavior trends, emotional sentiment, and awareness about a given topic. Once that data is processed new and valuable insight can be gleaned into the collective customer mindset from social media behavior.
Types of Text Mining
There are different types of text mining that can be used for analysis:
1. Word frequency
Used to find recurrent terms or concepts in a data set. Finding patterns of recurring words in the unstructured text is beneficial in analyzing customer reviews as well as feedback and social media conversations. For example, if the word “overpriced” is recurrent on customer reviews, it may be a sign that the business needs to adjust pricing.
A collocation is a sequence of words that are next to each other. Typically, collocations are either bigrams such as a pair of words (enterprise-wide, decision making) or trigrams, a combination of three words (go the distance, see you later). When text mining can identify collocations in a data set it improves granularity and delivers better results.
This is used to identify the context of a word. Since human language has so many ambiguities and nuances, concordance can recognize the exact meaning of a word in a particular context.
How NLP is Used in Text Mining
NLP is a subset of AI that deals with communication. It can be powerful when combined with text mining to read information and identify what is most important. What is impressive about NLP/text mining is the sheer volume of data that can be analyzed across millions of documents in a data set for meaning and patterns.
Text mining And NLP Advantages:
· Saves time and resources
· Higher efficiency and less margin of error than human analysis
· Managing information flow
· Gather insights into valuable data
Text mining is a powerful resource for businesses to gain insights into large data sets and evaluate opportunities that are going to drive the business forward.
Text analytics vs. text mining can be distinguished by the fact that text mining works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze surveys and reviews to see if customers are happy with a product. Text analytics can yield deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. Text analysis is an evolving technique that is revolutionizing the global economy. Part of this evolution has been the development of an array of techniques to derive meaning from text.
Text Analytics And Text Mining Use Different Approaches
While text analytics and text mining are often used interchangeably, they differ in that text analytics delivers quantitative insights and text mining delivers qualitative insights.
Text Analytics Approach
– Quantitative insights: rely on statistical modeling for “out of the box” solutions to any text analysis problem.
Text Mining Approach
– Qualitative insights: use linguistic rules to deliver complex outputs with more precision.
Text Analytics vs. Text Mining Examples
Texting Mining Used For Sentiment Analysis
Text mining is an older technique than text analysis. It has found an application in sentiment analysis or opinion mining on social media. Social media is a perfect breeding ground for text mining with:
– Facebook posts
– Instagram posts
– LinkedIn posts
Sentiment analysis uses Machine Learning and NLP to automatically analyze text for the sentiment of the writer. A single tweet can have a positive, negative, or neutral connotation for a brand based on the analysis of the sentiment of the writer.
– For example, the more information a business has about customers at its disposal, the more it can determine whether customers are happy with a product.
The unstructured data of social media posts presents an opportunity for businesses to become more responsive to customers and sustain long-term growth.
Text Analytics Used For Linguistic Approaches
Text analytics uses a linguistic approach for extracting value from unstructured textual data. Text analytics is more complex than text mining requiring sophisticated taxonomies or other structured lists as guidelines.
Once those guidelines are in place, text analytics works on large collections of unstructured data to discover new insights. Whereas text mining is better used to solve a particular problem, text analytics looks at the big picture of a problem.
– For example, a large data set of tweets are analyzed to predict a trend in customer behavior.
Actions made on data rather than guesswork enables informed decision-making that revolutionizes the course of business operations.
When it comes to leveraging the untapped value of unstructured data, text analytics and text mining are complementary techniques with different approaches. Text mining uses statistics while text analytics uses linguistics to wrangle unstructured data. Each can be practiced independently yet the most effective solutions combine their strengths.
Businesses can benefit from the balance of precision of linguistically based text analytics and the powerful recall of statistical text mining. With such a combination, great progress is being made on the future of text analytics in the world. A variety of textual analysis challenges can be met with text mining and text analytics leaving room for innovation.
Text analytics is shaping the future of how data is used to do business.
Using unstructured data (free form text that is not organized), text analytics can provide valuable new insights to help businesses:
– make data-driven decisions
– simplify data
– become more responsive to customers
The benefits of text analytics are redefining societal progress across industries. It happens when businesses identify, prioritize, and leverage text analytics results with real-world applications to the core of each business.
Identify Value In Unstructured Data
Text analytics enables businesses to analyze previously untapped large sets of unstructured data. Traditionally, unstructured data is difficult to get to and hard to work with. It is labor-intensive like “data trapped in virtual or physical filing cabinets”.
Discover New Opportunities In Your Text
It is estimated that 80% of unstructured data has not been accessed by businesses.
By using text analytics, businesses discover new opportunities to mine and analyze large data sets of customer feedback to identify trends in behavior from:
– social media
– live chats
This newfound information takes the place of guesswork when it comes to concerns and decision-making for the business.
Prioritize Consumer Information
It also enables the business to be more sensitive to the changing needs of customers. Text analytics empowers businesses to prioritize decisions based on informed results. For a business, knowing the “who, what, where is being talked about” is part of the solution. Being able to prioritize that direct, verbatim information and execute on it is what makes text analytics transformational for businesses.
Leverage Text Insights
When a business discovers text analytics, it can take a single insight into a customer’s mindset to cause a paradigm shift in business operations. A business experiences increased productivity, revenues, and cost savings with the ability to:
· Improve user experience
· Enhance business intelligence
· Conduct market research
· Detect product issues
· Monitor brand reputation
Other than practical applications, text analytics has strategic benefits for businesses.
Simplify Unstructured Data
For one, text analytics makes unstructured data simple. Now, it is easy to filter, search, and cross-reference unstructured data in an instant making it much more scalable.
Scale Your Enterprise
It is precisely this scalability that makes text analytics an effective enterprise-wide solution for data-driven decisions. It has no organizational boundaries making it a process that can create a foundation for enterprise-wide analytics.
Innovate Your Enterprise With Text Analytics Insights
Today’s business climate can change drastically overnight, in response to new technology, feedback, or innovation. Text analytics can empower businesses to find new opportunities and gain a competitive advantage. A benefit of text analytics is accessing new insights that were previously unknown or unavailable to the business. This capability drives business innovations into new markets and products.
The new generation of text analytics tools, such as the Content Analytics Platform (CAP) from Scion Analytics, leverage Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies to enhance capabilities for quantitative insights.
The bottom line is that there are many benefits of text analytics for businesses. It enables enterprises to uncover hidden signals in data to make smarter decisions. Confident and prompt decisions not based on guesswork sustain the long-term growth of the business. This is the way text analytics is shaping the future of businesses across industries.
Text analytics is the process of breaking apart textual content into conceptual areas to uncover insights, trends, and patterns once the content is digested and transformed. As a technique, text analytics allows businesses to “connect the dots” with data they did not even know they had to get into their customer’s minds.
Text Analytics Uses These Unstructured Forms Of Data
– Word documents
– PowerPoint decks
– Excel files
– Geospatial data
– Sensor data
– Voice messages
Most companies have a generous source of unstructured data that accumulates over the years on internal mapped drives or other locations.
Unstructured data is free form data that is not organized in a pre-defined manner consisting of documents of written content such as articles or specifications documents.
Unstructured information is typically text-heavy but sometimes features dates and numbers. The nature of unstructured data makes it challenging to get to, like being “trapped” in physical or virtual cabinets. Moreover, unstructured data is labor-intensive to index, structure, and report on. As text analytics evolved into a more comprehensive technology, Artificial Intelligence (AI) and Natural Language Processing (NLP) tools have been brought in to help with content analysis.
This development is especially relevant for how technology tools gain insight into unstructured data. For example, text has structure, such as the rules of grammar and spelling. However, true textual analysis requires an understanding of context and tone. Computers have yet to understand the full subtleties of language.
With so much data in text form, businesses are leveraging text analytics to liberate value within the text to make decisions and act. However, a decision or action made on incomplete data is a barrier rather than an accelerator to the evolution of a business.
How Text Analytics Provides Value With Unstructured Data
Less than 20% of unstructured data is analyzed by companies
Text Analytics is an evolving technique with practical application that is revolutionizing industries
Uses large text collections to identify trending patterns in data for new insights
Using unstructured data in text analytics is projected to change the future, with a report that predicts a North American industry valuation of $4.75 billion by the year 2024.
This enormous growth is driven by several factors, including:
· Fast advancements in the AI, machine learning, and text analytics industries
· Increased activity on social media platforms and demand for social analytics
· Widespread use of mobile devices and other mobile technologies
Text Analytics Benefits
As tech analytics shapes the future, it is important to understand the text analytics benefits of the technique in the real world. At the core of each business, the following text analytics benefits can be expected:
Spot emerging trends or concerns about your business
Rank issues based on what is important to your customer
Capitalize on insights into your customer segments and feedback
The application of text analytics makes a business more user-centric, responsive, and agile. A single insight into your customer’s mindset can cause a paradigm shift for the way business operates, in the following ways:
o Improve user experience and decision making
o Enhance business intelligence
o Help businesses to understand customer trends, product performance, and service quality
o Increase productivity and cost savings
Text Analytics Examples
Other than business applications, text analytics has also been used for a variety of government and research needs such as searching documents relevant to daily activities.
More specifically, government and military groups use text analytics for national security and intelligence purposes.
Exciting developments in scientific research have incorporated text analytics to organize large sets of text data i.e. unstructured data. Text analytics has enabled scientific discovery and sentiment analysis in social media that resulted in societal progress.
The gains that society has made by harnessing unstructured data can be attributed to the accessibility of the internet. In 2020, it is estimated that 4.57 billion people have access to the internet. That figure represents more than half of the world’s population. Out of which, about 49% of the population are active on social media. The large data sets available from social engagement have fueled text analytics. A vast amount of text data is generated every day in the form of surveys, reviews, blogs, and tweets. Add in digital customer interactions and significant use cases for text analytics are generated every day.
An example, in the healthcare space, text analytics was utilized for virtual doctor routing.
A text analytics model helped:
– Identify health conditions for patients based on a description of symptoms
– As the patient self identifies, they are routed to an appropriate virtual doctor
– This changes the paradigm and service delivery of healthcare providers
As industries learn lessons from implementing text analytics, they see new growth trajectories and innovate.
Another use case of text analytics is in finance. It is estimated that the price of non-compliance in finance is as high as $39.22 million a year in lost revenue, business, disruption, and penalties. Financial advisors are in danger of noncompliance when they fail to provide proper disclosures in “client advice” documents. Such disclosures may cover conflicts of interest, commission structure, cost of credit, and more.
In finance, text analytics uses AI to structure the data of disclosures into an easy-to-review form like an Excel document. As a result, human auditors that might spend hours reviewing documents can process the same information in less time. Finance can leverage text analytics solutions to turn hours of work into minutes of productivity.
Steps To Text Analytics
Productivity gains are a result of analytical processes that vary based on specific text analysis tools. However, certain fundamental steps are always present in text analytics. Here are the steps in the text analytics technique:
In the first step of how text analytics works, identify locations from which to source data.
Next, the text collection is parsed. Parsing is the process of understanding the syntactical structure of the data set. The syntactical structure is based on the structural organization of a given sentence. The structural organization of a sentence is dependent upon the grammatical function or meaning of a sentence. Therefore, parsing looks at the arrangement of words, phrases, and clauses in the sentence of the text for further analysis.
After the text analytics technology transforms the text into structured data, Natural Language Processing (NLP) technology generates the needed metadata and understanding. This happens when the extracted text is transformed into machine-readable formats.
The critical step for decision making, mining happens when Machine Learning or AI algorithms identify insights across user-bases and buyer personas. This allows businesses to act based on tangible data results rather than guesswork.
Text Analytics Uses These Types Data Mining
This type of mining categorizes phrases as positive, neutral, or negative. This process yields valuable insights about market conditions and customer perceptions of the business.
This type of mining is based on spotting trends. Specific user desires are assigned to statements and calculated as trends. These trend findings can be used to personalize service offerings.
This type of mining is a big picture trend overview. Large textual data sets are used to identify significant trends in consumer behavior across an industry or product.
This type of mining is focused on isolating specific ideas from documents and ranking them by predetermined criteria. Concept mining is effective for garnering insight into consumer-driven operational needs for businesses.
Text Analytics vs. Text Mining
An understanding of how text analytics works is important in assessing the potential of data-driven growth in a globalized economy. Text analytics is an opportunity to discover the unexpected in the voice of your customers and innovate your business. While text analytics is often used interchangeably with text mining, these are not quite the same concepts.
Text analytics vs. text mining can be distinguished by the fact that text mining works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze surveys and reviews to see if customers are happy with a product. Text analytics can yield deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. For example, text analytics can be used to interpret an upswing in the popularity of a product over time.
Imagine what you can do with text analytics for your business.
According to Markets and Markets, the text analysis industry will be worth $22 billion by 2022. This technique has changed the way the world interprets trends, consumers behave on social media and in real-time, and businesses operate. Text analytics has moved society forward into a bright future as part of a solution that changes the way we live.
Seeing exciting new results from text analytics poses the question: are all text analytics solutions created equal? User experience and a robust industry show that each text analytics platform has a specific offering and use cases where it excels.
At Scion Analytics, we are a leader in partnering with companies to transform and scale their business by harnessing the Content Analytics Platform (CAP). The CAP pioneered using AI and NLP to enhance the capabilities of text analytics.
We empower people to liberate value in the content. Together, we are on a transformational journey with content analytics becoming a ubiquitous form of communication. With personalized support for CAP, global businesses can increase impact, extend technical capabilities, improve operational efficiency, and scale their business. We are building a bright future together through technology.