Text Mining Examples

Text Mining Examples

As the amount and relevance of unstructured data increases, so text mining is a necessity. Applications like Risk Management Software have improved profits thanks to text mining’s ability to advance our understanding about risks associated with cybercrime or financial issues.

Text mining is a technique that can extract useful information and knowledge from unstructured text, such as business deals or customer reviews. Unstructured data is simply content that is not yet neatly placed into a spreadsheet format. Text mining is being used in large organizations to discover insights about trends within their data sets.

Text Mining is an innovative technology that can help organizations. The following ten text mining examples give you a glimpse into its potential for the future:

Risk Management

With the rise in concern over cyber-attacks, it is more important than ever to protect against risk. One way that this can be done successfully is by using text mining technology which identifies potential security threats. It analyzes vast quantities of documents and data streams that are stored electronically or on paper together with information sources such as images or sounds.

Knowledge Management

Rather than trying to find information in a sea of documents, knowledge managers use software that searches texts for keywords and phrases. This allows them to quickly sift through large volumes of data with the goal being that of product development.

Cybercrime Prevention

The anonymity of the Internet and many of the communication channels allows opportunities for cybercrime. Text mining coupled with anti-crime applications are making an impact on Internet-based crimes.

Customer Care Service

Many customers are now using text analytics software to improve their experiences with the help of various sources. These may include surveys, trouble tickets, and customer call notes. These sources are valuable information that can be used by companies for better quality service delivery and faster resolutions.

Fraud Detection Through Claims Investigation

Text analytics is a tremendously effective technology in any domain where most of the information may be found as text. Insurance companies are taking advantage of it by using this kind of research. It combines results from mining through texts with other structured data to prevent frauds and efficiently process claims.

Contextual Advertising

Digital advertising is a new and growing field for text analytics. Unlike cookie-based approaches, contextual advertising provides better accuracy as well as preserves the user’s privacy.

Business Intelligence

The decision-making process is a delicate balance between strategy and analytics. The large companies use text mining for this because it helps them make quick decisions on data volumes that are too broad or complicated to analyze traditionally.

Content Enrichment

Text analytics is the process of discovering information from texts. It can be used to enrich content, provide tags for organizing, and summarizing available information in a scalable way that makes it suitable for many purposes.

Spam Filtering

Email is an effective, fast, and affordable way to communicate with your customers. But it does come with its downside: spam. Today’s issue of email service has led Internet providers increasing their costs for managing these emails as well as updating hardware and software. Text mining techniques can be implemented towards improving statistical-based filtration methods.

Social Media Data Analysis

Social media has been one of the most important sources for businesses to gain intelligence on how their customers feel about them. Companies are using prolific social media data to better understand what people want and need from a brand or product they may be considering to purchase. This includes things such as text analytics, which can analyze large volumes of unstructured texts to extract sentiments, opinions, and emotions regarding brand products.

Text Mining Future

Text Mining Future

What Is Text Mining

Text mining (also referred to as text analytics) is an Artificial Intelligence (AI) technology that uses Natural Language Processing (NLP) and information extraction. It transforms free-form unstructured documents into normalized structured data suitable for analysis. Text analytics uses Machine Learning (ML) algorithms in order make predictive propositions about future actions based on what has been recorded textually.

Future Of Text Mining

Text mining implementation was initially slow to gain traction. Originally, there were only loosely integrated and independent solutions available. In many cases, companies did not quickly see the true value behind sophisticated analytical solutions. We’ve seen text analytics adoption rates increase, and we expect this trend to continue. 

There is an uptick in free-form data. High technological advancements like automated text analytics can find value from this unstructured information much faster than humans could. 

Not every company will adopt technology to analyze unstructured data. There are many reasons why, including:

Structured data Is The Backbone Of Research

This means unstructured or semi-structured data will have a big impact on the field if it does not change with the times.

Unstructured Data Is Like Water

It is difficult to extract, measure, or analyze because it is found in many sources, such as social media or company web sites.

Not All Feedback Should Be Treated Equally

For example, capturing and analyzing unstructured social media posts from certain sources may present challenges for an organization’s code of conduct.  This is because they are not able to follow basic standards when collecting this type or information in a systematic way. 

Benefits Of Text Mining

The benefits of text-based communication are undeniable, but there can be no ignoring the increase in unstructured data that this represents. 

Some key benefits are:

Mining Text For People’s Thoughts

Text is often used to express thinking and feelings. It is how people convey ideas or reasoning from one person to another. Customers’ thoughts now have more value than ever before because they are using texting as their primary method of recording these thoughts and feelings.

Mining Text For Genuine Insights

The idea of being able to get genuine insight into what someone is thinking may sound too good to be true. Many argue that technology can never pick up on nuances like sarcasm, irony, and more with pinpoint accuracy. Certainly not as well as a real person does. But there are those who believe this tool has potential. Assessment of text analytics solutions is catching up with what experts have known these past few years: these are the future. They could be just as good or better than human analysts at interpreting data without any bias.

Mining Text for Relevant Categories

Organizations need tools that will allow them to mine the text data for relevant categories. Besides content, they can automatically determine sentiment by category, and correlate insights across all feedback channels.

Gain Insights from Both Positive and Negative Feedback

The use of text analytics solutions allow organizations to reach a whole new level of insight into what people are saying about them. These tools offer businesses several abilities. They can match sentiment from both positive and negative feedback, analyze social media conversations across categories, and receive timely alerts with social media updates.

Gain More Value with Deep Learning

Deep Learning techniques could help text analytics determine the meaning of words and their association with other ones, which would improve its adaptability. It can also automatically categorize sentences into topics as well as detect sentiment for that topic. These techniques can make it easier to use in different domains or languages.

Conclusion

Text Mining is like mining for gold for data-driven businesses. It is essential to extract structure from unstructured data using text analytics. The Content Analytics Platform (CAP), developed by Scion Analytics, can quickly analyze a textual document in any format, restructuring it for value.

Common Mistakes In Text Analytics

Common Mistakes In Text Analytics

There are common mistakes made when a data analyst performs text mining (or text analytics). Text analytics is the study of written text for information- mining purposes. This includes linguistic, statistical, and machine learning techniques that can model human language in order to extract insights from it. Text analytics is a powerful tool and can be transformational for businesses.

Not Asking Target Questions

In order to get the most out of your survey, you need an initial question. Even if it is a simple and straightforward “What do my guests think about these rooms?” or even something more complex like “How could I improve this lobby for future visitors?” Asking a targeted question will get the useful information that is desired.

Isolated Data

Last year, 25 guests complained about the bathrooms. Is this better or worse than your competition? Is this better or worse than last year? You need to make comparisons either across competitors or time (preferably both) in order for you to know if there is anything wrong and what needs redressing.

Acting Slowly On Individual Complaints

Along with getting a view of all discussions related to your brand, you can also use text analytics and sentiment analysis in order to throw an alert if someone has Tweeted that they’re having a poor experience. The first case (the tweet) is vital because oftentimes this type of feedback happens right now.

Acting Too Quickly On Broad Analysis

Don’t just do a surface-level analysis. Spend time analyzing the feedback and why it matters to you. Dig deeper if needed, but don’t forget that this is also an investment of your valuable marketing resources. Look at trends for the broad analysis. Evaluate the complaints against the competition and see if they demand immediate attention.

Assuming Clean Data

Data quality is always an issue and should not be assumed to be good quality. Even with the help of a review analysis system, it might be worth looking into the incoming data stream to make sure weird stuff is not making its way in.

Unrealistic Expectations Of The Text Analytics System

According to one university study, humans will disagree about at least one sentence out of five as to its sentiment classification. Is that statement’s mood to be seen as good, neutral, or bad? The accuracy of a text analytic system will be dependent on its fine-tuning. It is also dependent on the system’s inter-rate agreement. Resolve to have only 85% accuracy.

Not Appreciating Multiple Conversion Steps

Hand-written comments and speech-to-text conversions are not easy. If you want the most accurate data, make your own transcriptions. Use an Optical Character Recognition (OCR) system or convert from one type of file format to another with relative accuracy for each step.

Not Allocating Any Tuning Time

Time should be spent fine tuning the text mining system for details and itemized concerns, such as restaurant menu choices or the brand of furniture used in a hotel.

Thinking Too Small

After the initial question targeted question, mentioned above, there should be an expansion out of more questions. Mine the text for what people are saying about you and about your competitors.

Conclusion

Text mining is a valuable practice for any data-driven business. Text mining with the smart disciplines of asking the right questions, awareness of limitations, and fine tuning your mining habits will prove even more valuable.

Text Mining Social Media

Text Mining Social Media

Social media can provide a treasure trove of insights for brands looking to understand consumers. Social media data holds immense potential for marketers. But to make the most of it, you need powerful analysis and insights from text analytics.

There is an ever-growing amount of social media posts flooding into our feeds every day, along with the rise in popularity regarding image-based platforms like Instagram or Facebook. There have never been more reasons than now why companies should invest time and money into analyzing what people say about them online. People comment both positively (like customer reviews) but also negatively through comments sections under articles.

What Is Text Mining?

Text mining (or text analytics) is the process of deriving information from text sources. Text analysis can be applied to any type, size, and complexity of datasets–from social media posts on Facebook or forum discussions over in Yahoo Answers. It can be applied to surveys submitted by participants through online forms as well as transcripts recording phone calls between customer service representatives and clients.

For years, computers have struggled to understand natural human language due to its subjectivity and quirks. But with new technology comes progress–text analytics are now more accurate than ever before.

While humans are still better at understanding language, the vast amount of text data makes automated analysis solutions particularly useful for processing large volumes.

Text Mining In Social Media Examples

Social media data can be a gold mine for businesses looking to gain insight into their customers and potential clients. Text analysis is one way this information gets translated from text messages or posts on platforms like Facebook or Twitter. Text mining is done to answer all kinds of questions about consumers as well as brands themselves. It answers such questions as what products they prefer, how often certain topics come up during conversations, where people live based on where friends go.

Sentiment And Emotion

Understanding the general sentiment or specific emotions expressed about a brand, product, or topic can help you better cater your business message to customers.

Measure Share Of Voice

Text analysis is a way to get a representation of what people are really talking about. You can see what percentage of a conversation is about a certain product or brand. You could use this information for your marketing campaign or even just as simple research.

Identify Key Words, Topics, And Phrases

Text analytics can drill down into any conversation to understand what is driving it and how the content of that specific discussion has changed over time. With this information, your success rate at getting results from marketing will increase dramatically.

Quantify Intent To Purchase

The intent to purchase is a very important stage in the process of becoming an actual consumer. It happens when someone first becomes interested enough with your brand, products, or services that they are thinking about buying them and might even act on those thoughts.

Conclusion

Text mining social media is a valuable practice for any data-driven business. It provides businesses an ability to be aware of how their products or services are appreciated by the consumers. This practice lets companies know how to improve or limit their offerings.

How Does Text Mining Improve Decision Making?

How Does Text Mining Improve Decision Making?

Text mining is an Artificial Intelligence (AI) technology that uses Natural Language Processing (NLP), Machine Learning (ML), sentiment analysis, and information extraction.

Text mining is the process of deriving high-quality information from texts. Text mining typically does this through devising patterns and trends in a document by using statistical pattern learning techniques

Text is often used to express thinking and feelings. It is how people convey ideas or reasoning from one person to another. Sentiment analysis data mining can detect these feelings. Text analytics uses ML algorithms to make predictive propositions about future actions based on what has been recorded textually.

The documents already inside an organization can provide early warning of risk and compliance issues. They can bring new insights into what their customers really need, whether it be features they want or areas in which to improve their service quality for lower prices.

Deep Learning is a set of neural networks that allow computers to learn on their own. The idea behind deep learning comes from decades-old research into how human brains work.

Big data is the enormous amount of unstructured and varied forms of data produced continually. When it comes to big data, it first transforms the vast free-form unstructured data into the machine language of numbers suitable for analysis.

Compliance, Risk, And Threat Detection

In a world where financial security is at stake, text mining can be used to detect potential compliance issues or provide early warning of fraud and criminal activities. This includes money-laundering in the finance industry which could result from insufficient risk analysis. With the help of text mining, public sector organizations can use this technology to spot potentially dangerous issues before they cause real harm.

Customer Engagement

Companies’ interactions with customers generate mountains of text. Whether it’s through email, social media, or notes in the CRM system. Text mining can produce early insights into what clients are thinking, which saves money by reducing dependence on call centers.

Text mining helps companies take proactive measures by getting early warning about issues that could damage their reputation. They can also build new revenue streams through identifying products that clients need or segments in an untapped market.

Better Business Decisions

Text mining can help by providing more accurate insights across a broader range of documents and sources. This approach is especially powerful when combined with external data sources. These are often not curated as closely internally for accuracy because they come from different areas within your company or other companies. Bringing these two types together helps improve both the speed at which decisions get made as well as their competency in those conclusions.

Conclusion

Text Mining is like finding gold in a stream. Text analytics is necessary to extract structure from unstructured data and use it for value creation. It is like other valuable resources such as oil or natural gas extraction processes. The Content Analytics Platform (CAP), developed by Scion Analytics, can quickly analyze textual documents across formats which restructures them into their most relevant pieces.

Text Analytics For Finance

Text Analytics For Finance

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:

1. Investors

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.

Conclusion

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.

The Importance Of Textual Analysis

The Importance Of Textual Analysis

What Is Textual Analysis

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:

1. Scalability

 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.

Conclusion

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.

Text Analytics, Nuns, and Language Density

Text Analytics, Nuns, and Language Density

The Age of Text Analytics

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.

What is Text Mining?

What is Text Mining?

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:

– Emails

– 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.

2. Collocation

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.

3. Concordance

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

Text Analytics vs. Text Mining

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:

– Tweets

– 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.

Conclusion

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.