Text mining or text analysis is useless without NLP. It intends to deliver practical, persistent, and credible insights with machine learning. The key objective is to arm businesses with real-time insights that help them drive innovation as well as rack up customer service and profits. The rapid growth in the requirement of understanding the customer will result in an increase in text analytics tools.

1. What Is Text Analytics?
Tools, Examples, and
Applications
With the advent of emergent technologies, businesses are having to
hustle to sustain momentum. While it may seem a little
overwhelming to start with, businesses that are reinventing and
adopting such technologies stand to benefit greatly in terms of
sharpening their competitive edge by being able to understand their
customers and their needs in an unprecedented fashion.
Like data analytics services, text analysis is one such advancement
that promises to take businesses to the next level with the assistance
of NLP and AI.

2. So, what is Text Analytics?
Text analytics is a sub-set of Natural Language Processing (NLP) that
aims to automate extraction and classification of actionable insights
from unstructured text disguised as emails, tweets, chats, tickets,
reviews, and survey responses scattered all over the internet.
Text analytics or text mining is multi-faceted and anchors NLP to
gather and process text and other language data to deliver
meaningful insights.

3. Why is it needed?
Maintain Consistency: Manual tasks are repetitive and tiring. Humans
tend to make errors while performing such tasks – and, on top of
everything else, performing such tasks is time-consuming. Cognitive
biasing is another factor that hinders consistency in data analysis.
Leveraging advanced algorithms like text analytics techniques
enable performing quick and collective analysis rationally and
provide reliable and consistent data.
Scalability: With text analytics techniques, enormous data across
social media, emails, chats, websites, and documents can be
structured and processed without difficulty, helping businesses
improve efficiency with more information.
Real-time Analysis: Real-time data in today’s world is a game-
changer. Evaluating this information with text analytics allows
businesses to detect and attend to urgent matters without delay.
Applications of Text analytics enable monitoring and automated
flagging of tweets, shares, likes, and spotting expressions and
sentiments that convey urgency or negativity.

4. Text Analytics Techniques
Word / Term Frequency
This nifty text mining technique captures frequently occurred words
across a data set and rates their importance accordingly. To give a
context: consider a customer feedback data set on a recently
launched product. If the word ‘good’ appears more frequently, this
may mean that the customers greatly like the product.
The biggest challenge with word/term frequency is that different but
related terms aren’t checked. Different words may be applied to
mean the same thought, but this can be reckoned as two different
topics and rated accordingly, leading to disparities in coming to a
conclusion.
Collocation

5. Collocation focuses on identifying commonly co-occurred words. In
other words, bigrams and trigrams. Bigrams are two adjacent words,
e.g., customer service, while Trigrams are three adjacent words, e.g.,
near the hotel. The understanding can help underpin semantic
structures and improve the granularity of insights.
Sentiment Analysis
As the name suggests, sentiment analysis help understands the
impact of a product/service on the customer’s sentiments. Also, it
helps compare the customer’s sentiment towards one
product/service against that of a competitor.
Three are three steps to perform sentiment analysis extensively:
Polarity Analysis: Measuring the tone of the data, i.e., positive or
negative.
Categorization: Ranking the data in a specific metric, i.e., positive –
happy, excited, pleased; negative – sad, angry, confused, frustrated.
Scaling: Scaling the emotions on a measure of 0 to 10.
Although sentiment analysis aims to glean valuable insights by
contextualizing data, the biggest challenge with sentiment analysis is
to spot sarcasm or irony and software is currently being developed
to overcome this challenge.

6. Text Classification
The most advantageous NLP (Natural Processing Language)
technique, it is language-agnostic. It can order, structure, and
segment pretty much any data. Text classification helps assign
predefined tags or categories to unstructured data. Sentiment
Analysis, topic modelling, language, and intent detection all come
under the text classification umbrella.
Topic Modeling
Topic modelling helps categorize documents based on specific
topics. Topic modelling helps process different documents and
abstract recurring themes and is less individualized. Topic modelling
classifies and gives a percentage or count of words of each
document assigned to a specified topic.
Named Entity Recognition
Named Entity recognition helps identify nouns with data sets.
Consider numbers accompanied by ‘INR’ as being monetary;
likewise, “Ms.” or “Mr.” or “Mrs.” followed by one or more capital words
is probably a person’s name.
The major challenge is that, although some nouns define important
categories like geographic location, name, or monetary values, some
lack abbreviations, leading to a lot of confusion.
Applications of Text Analytics
Regardless of the industry, applications of text analytics can be of
substantial support for businesses for understanding as well as
reaching out to customers comfortably. Check below some of the
use cases/benefits of text analysis for businesses.

7. #1. Social Media Listening
Apart from being a medium of staying connected, social media has
also become a platform for branding and marketing. Customers talk
about their favourite brands and share their experiences all across
social media. Leveraging sentiment analysis of the data available on
social media with the help of text analytics tools helps to understand
the positive or negative sentiments of users towards
products/services and the impact and relations of brands with its
customers. Furthermore, social media listening can enable brands
to build trust with customers.
#2. Sales & Marketing
Prospecting is a nightmare for a sales team. Sales teams make every
effort to improve sales and performance. According to a
MonkeyLearn study, 27 % of sales agents spend more than an hour a

8. day on data entry work instead of selling, signifying critical time is
lost in administrative work and not closing deals.
Text analytics techniques help reduce this menial work with
automation while providing valuable and specific insights to nurture
the marketing funnel.
To give you an idea: Chatbots are employed to cater to customer
queries in real-time. Analyzing this data helps the sales team predict
the likelihood of a customer buying a product, perform target
marketing and advertising, and make product adjustments.
#3. Brand Monitoring
Businesses fight tooth and nail to establish and brand supremacy.
Today, professionals are paid to write false or hype reviews across
the internet and social media. Also, sometimes customers often write
angry reviews in the spur of a moment. Such reviews often spread
across the internet like wildfire and do unmitigated harm to a
company’s brand image.
Negative reviews often drive away customers. Studies show, 40% of
consumers are put off from buying a product/service if there is a
negative review.
Visual web scrapers and web scraping frameworks in text analytics
empower brand monitoring, comprehending one’s brand evolution,

9. and pinpointing aspects affecting one’s brand in real-time, thus,
enabling businesses to take necessary action immediately.
#4. Customer Service
Businesses constantly endeavour to facilitate seamless customer
service. Much of the customer churn factors occur due to customer
service flaws. With text analytics tools, you can scrape together
customer concerns/queries and feedback to streamline customer
service processes. Aside from improving responsiveness, this can
also help automatically route tickets to reduce manual work and
errors. To give an example: the algorithm draws a point ‘My order isn’t
delivered yet’ out of customer queries – this will be compared and
matched with a Delivery Issues tag automatically with the assistance
of text analytics tools.
Additionally, text analytics tools will also help establish personalized
customer services, employ the right person for the job, and set
priorities efficiently.
#5. Business Intelligence
Although businesses can glean “what is happening?” with data
analysis, they struggle to figure out “why this is happening?”.
Applications of text analytics help businesses obtain context out of
the numeric data and reason out why a situation has happened or is
happening or what may happen in the future. Case in point, a large
number of factors contribute to sales performance. While data
analysis will provide one with numerical statistics, text analytics
techniques will help determine why there is a drop or rise in the
performance.

10. #6. Product Analytics
Text analytics not just helps in understanding customer needs, but
also helps in improving the product. Analyzing customer reviews
gives a clear picture of what exactly the customer is looking for vis-
à-vis a product and what they think about the competitor’s product.
It enables businesses and brands to build quality products that meet
customer requirements.
#7. Knowledge Management
We suffer from an overabundance of data today. Processing this
behemoth data to draw actionable insights in less time is hardly
possible without sophisticated technology advancements. This puts
time-sensitive professions like healthcare in dire straits. However, text
mining or text analytics techniques can help sort through surplus
data in a short time and provide valuable insights for real-time
solutions and efficiency.
#8. Email Filtering
Text mining can help detect junk, spam, or malicious emails,
preventing fraud and other kinds of cyberattacks.
Text Analytics in 2021
Text mining or text analysis is useless without NLP. It intends to deliver
practical, persistent, and credible insights with machine learning. The
key objective is to arm businesses with real-time insights that help
them drive innovation as well as rack up customer service and
profits. The rapid growth in the requirement of understanding the
customer will result in an increase in text analytics tools.