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text analytics

You’re probably familiar with the challenges of evaluating big amounts of unstructured text data, such as reviews, emails, and social media posts. Manually processing and organizing text data takes time, is tedious, inaccurate, and can be costly if more staff is required. In this article, we will discuss text analytics, including what it is, how to utilize AI tools to perform text analysis, and why it is more important than ever to automatically review your content in real-time.

text analytics

What is Text Analytics?

Text analytics is a machine learning technique that automatically derives important insights from unstructured text data. Businesses utilize text analysis technologies to quickly ingest web data and documents and translate them into actionable insights.

Moreover, text analysis may be used to extract particular information from hundreds of emails, such as keywords, names, and information, or to categorize survey replies based on attitude and topic.

So, text analytics vs. text analysis: what's the difference?

Text analysis yields qualitative findings, whereas text analytics yields quantitative outcomes. A text analysis machine discovers crucial information inside the text itself, whereas a text analytics machine reveals trends across hundreds of texts, resulting in graphs, reports, tables, and so on.

Assume a customer service manager is interested in seeing how many support tickets each team member has addressed. In this scenario, text analytics would be used to build a graph depicting individual ticket resolution rates.

The manager, on the other hand, is likely to be interested in knowing what proportion of tickets resulted in a favorable or bad consequence.

Customer service managers may evaluate the wording of each ticket and subsequent responses to see how each agent handled tickets and if customers were pleased with the outcome.

The problem with text analysis is fundamentally deciphering human language ambiguities, whereas the problem with text analytics is finding patterns and trends from numerical data.

Why is text analysis important?

The insights and advantages gained from using machines to organize and analyze text data are vast. Let’s have a look at some of the advantages of text analysis below.

-Text Analysis Is Scalable

Text analysis technologies allow enterprises to arrange massive amounts of information, such

as emails, chats, social media, papers, and so on, in the blink of an eye, allowing them to focus on more critical business duties.

-Analyze text in real-time.

Businesses are inundated with information, and client comments may surface everywhere on the internet these days, making it tough to keep track of everything.

-Methods and techniques

We have basic and advanced text analysis techniques, each with its own set of goals. Here are some easy analytic tools and how you may utilize them. Text Classification

1) Text Extraction
2) Word Frequency
3) Collocation
4) Concordance
5) Word Sense Disambiguation
6) Clustering
7) Text Classification

The practice of classifying a given text into one or more predetermined categories or labels that supply useful data and solve issues is known as text classification. Natural language processing (NLP) is a machine-learning approach that allows computers to decipher and comprehend text in the same way that humans do.

We’ll go through some of the most typical text classification problems, including sentiment analysis, topic modeling, language identification, and intent detection.

Sentiment Analysis

Customers share their thoughts on firms and goods through customer service contacts, surveys, and the internet. Sentiment analysis employs strong algorithms to automatically analyze and classify opinion polarity (positive, negative, and neutral) as well as the writer’s moods and emotions, as well as context and sarcasm.

Companies, for example, can identify complaints or urgent requests using sentiment analysis. Sentiment classifiers use user input to enhance goods. Test Bytesview’s pre-trained classifier. Simply type in your own words to see how it works.

Topic Analysis

Topic analysis (or topic modeling) is another frequent form of text categorization, which automatically organizes text by subject or theme. For example “The software is simple to use,”

Try Bytesview’s tool for more.

Intent Detection

Text classifiers are machine-learning algorithms that are taught to categorize or classify incoming text. They are often used for sentiment analysis, spam detection, and topic categorization. They may be taught using a variety of methods, including supervised learning, unsupervised learning, and semi-supervised learning.

Try out Bytesview’s tool for more.

Text Extraction

The technique of automatically extracting specific information from a larger text document is referred to as text extraction. This may be accomplished through the use of several approaches such as natural language processing (NLP) and regular expressions. Named Entity Recognition (NER), is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, and so on, is one common method.

text analytics keyword

Entity Recognition

A named entity recognition (NER) extractor detects entities inside text data that can be persons, corporations, or locations. As Bytesviews pre-trained name extractor, the results are labeled with the relevant entity label.

How does Text Analytics work?

text analysis

It’s comparable to how people learn to distinguish between subjects, objects, and emotions.

Assume we have urgent and low-priority items to address. We don’t recognize the difference intuitively we learn gradually by connecting urgency with particular statements.

How to Analyze Text Data

Text analytics may stretch its AI wings across a range of texts depending on the findings you want. It can be used for:

-full documents: gets information from an entire document or paragraph. For example, consider the general tone of a customer review.

-Single sentences: collects data from particular sentences, such as the more precise feelings of each sentence of a customer review.

Once you’ve decided how you want your data to look, you can begin analyzing it. Let’s take a step-by-step look at how text analysis works.

Data Collection

Data on your brand, product, or service may be gathered from both internal and external sources.

-Internal Information

This is the information you collect daily, from emails and chats to surveys, client inquiries, and customer support issues.

Simply export information as a CSV or Excel file from your program or platform, or connect to an API to access it directly.

  • Customer service software is used to engage with customers, handle user inquiries, and resolve customer support issues.
  • CRM (Customer Relationship Management) software aids firms in managing interactions and connections with customers, clients, and sales prospects.
  • Email: The most common method for managing client relationships, emails are still a vital corporate communication tool.
  • Surveys (usually used to collect customer service feedback, product feedback, or market ratings) are one of the most prominent customer experience measures in the world. Many businesses utilise NPS tracking software to collect and evaluate customer feedback. Delighted,, and Satismeter are a few examples.
  • Databases: A database is an information collection. A database management system allows a corporation to store, manage, and analyse many types of data. Postgres, MongoDB, and MySQL are examples of databases.
  • Product analytics: feedback and information about your product or service interactions with customers. Understanding the consumer journey and making data-driven decisions are beneficial. ProductBoard and UserVoice are two solutions for handling product analytics.
  • External Information
    This is text data gathered from various sources on the internet. Web scraping technologies, APIs, and open datasets may be used to collect and analyse external data from social media, news reports, online reviews, forums, and other sources.

Web Scraping Software:

text analytics

-Visual Web Scraping Tools: With tools like, Portia, and ParseHub, you can create a web scraper even if you have no coding skills.

-Web scraping frameworks: experienced coders may use tools such as Scrapy in Python and Wombat in Ruby to construct bespoke scrapers.

You’ve learned how to use text analysis tools to break down your data, but what do you do with the results? Business intelligence (BI) and data visualization tools make it simple to grasp your outcomes (in decreasing order), but not why. Numbers are simple to understand, but they are also relatively limiting. Text data, on the other hand, is the most common type of company information and may give great insight into your operations. Machine learning and text analysis can automatically examine this data for fast insights.

Visualize Your Text Data

You’ve learned how to use text analysis tools to break down your data, but what do you do with the results? Business intelligence (BI) and data visualisation technologies make it simple to grasp your findings by displaying them on visually appealing dashboards.


The visualization data analysis tool is one of the most efficient and straightforward methods for extracting insights from unstructured text data. Get customized insights to help you improve marketing, customer service, human resources, and other areas.

Google Data Studio

Google’s free visualization tool lets you generate interactive reports from various data sources. After you’ve imported your data, you may use several tools to construct your report and transform your data into an eye-catching visual tale. Share the findings with people or groups, post them online, or embed them on your website.


Lastly, Looker is a corporate data analytics tool that delivers actionable insights to anybody in a firm. The objective is to provide teams a wider picture of what’s going on in their firm.

Text Analysis Applications & Examples

text analytics

Did you realise that text accounts for 80% of all corporate data? From support requests to product feedback and online consumer interactions, text is involved in every key company operation. With a wide range of commercial applications and use cases, automated, real-time text analysis may help you get a hold on all that data. Increase efficiency and eliminate repeated jobs, which frequently have a significant turnover impact. Without having to go through millions of social media postings, online reviews, and survey results, you may better grasp customer insights.

1. Social Media Monitoring

Assume you work for Uber and want to hear what customers are saying about the
company. You’ve seen both good and negative responses on Twitter and Facebook. However, 500 million tweets are generated every day, and Uber receives thousands of social media mentions each month. Can you picture manually examining all of them?
This is where sentiment analysis comes in to examine a particular text viewpoint. You may automatically categorize your social media remarks as Positive, Neutral, or Negative by evaluating them with a sentiment analysis model. Then, to comprehend the subject of each text, run them through a topic analyzer. You may automatically discover the reasons for favorable or negative remarks by executing aspect-based sentiment
analysis and gain insights such as:

  • What is the most common complaint about Uber on social media?
  • The success rate of Uber customer service – are consumers pleased or dissatisfied?
  • What do Uber consumers enjoy about the service when they talk about it positively?

Text analytics may be used not just to monitor your brand’s social media mentions, but also to watch your rivals’ mentions. Is a client complaining about the service of a competitor? This allows you to attract potential clients and demonstrate how much superior your brand is.

2. Brand Monitoring

Follow comments about your brand anywhere they may surface in real-time (social
media, forums, blogs, review sites, etc.). You’ll be able to utilize favorable remarks to
your advantage when anything unpleasant occurs.
Unfavorable reviews have a significant impact: 40% of buyers are discouraged from
purchasing if a company gets negative ratings. An irate consumer complaining about
bad customer service may quickly spread: a buddy shares it, then another, then
another… And before you know it, the bad remarks have spread like wildfire.

  • Recognize how your brand reputation changes over time.
  • Compare your brand reputation to that of your opponent.
  • Determine which issues are harming your reputation.
  • Determine which factors are enhancing your brand’s image on social media.
  • Identify potential public relations issues so you can deal with them as soon as

3. Customer Service

Contrary to popular belief, text analysis does not imply that customer support will be
fully automated. It simply implies that firms may streamline procedures for
teams to spend more time-solving problems that require a human connection. Businesses will be able to enhance retention in this manner, considering that 89 percent of customers switch brands due to bad customer service. But how might text analysis help your business’s customer service?
Allow machines to handle the heavy lifting. Text analysis recognizes and tags each ticket automatically. This is how it works:
The algorithm examines client language and terms such as “I didn’t get the appropriate
order.” Then it compares it to other chats of a similar nature.
Finally, it discovers a match and automatically tags the ticket. In this situation, it may be classified as Shipping Issue.

4. Sales and Marketing

Prospecting is the most challenging aspect of the sales process. And it’s becoming
increasingly difficult. The sales staff is continually looking for methods to clinch deals,
which necessitates making the sales process more effective. However, 27% of sales agents spend more than an hour a day on data entry labor rather than selling, implying that important time is lost to administrative work rather than making agreements. Text analysis eliminates the need for manual sales procedures such as:

  • Updating the offer status in your CRM to Not interested.
  • Lead qualification based on corporate descriptions.
  • Identifying social media leads who exhibit a want to buy.

Text Analysis Resources

  • Text Analytics APIs
  • Python
  • NLTK
  • SpaCy
  • Scikit-learn
  • TensorFlow
  • PyTorch
  • Keras


BytesView Sentiment Analysis tools

We have discussed what is text analytic and how it is used. If you are looking for best text analytic tool in the market then Bytesview is the best do check the product out here.

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