AI-based Text Analytics & Its Solutions - FutureAnalytica

AI-based Text Analytics & Its Solutions - FutureAnalytica

What is AI-based Text Analytics

Your business has access to innumerable data sources with feedback from your clients, customers, employees, vendors, and more. This unstructured data holds the key to achieving your customer experience goals, but it requires specialized solutions to analyze it properly. Text analytics technology introduces an automatic approach to analyzing and visualizing unstructured text data for qualitative measurements.

Imagine gaining actionable insights from every email, social media post, chat message, and survey. Text analytics allows your business to find out more about what people are talking about, thinking, and feeling when engaging together with your products and services.

The differentiation between Text Analytics, Text Mining, and Natural Language Processing:

To fully understand text analytics, you furthermore may need to know about text mining and natural language processing.

Text mining processes large sets of unstructured data to extract information from it. Without this tool, you’d have to manually work on screening written inputs and determining whether it’s quality data. Once this data is extracted into structured data, you’ve got a form that’s capable of going through analytics to look for valuable insights. Text analytics can create reports, identify significant patterns, and offer other ways in which businesses can make data-driven decisions.

Natural language processing is an essential technology used by text mining and text analytics. It’s a kind of artificial intelligence that is capable of processing human language into a form that is usable by computers. The top user doesn’t need to know specific keywords or syntax to make their request understandable by the machine on the other end. This technology uses a model that learns from the data fed into it. The accuracy and relevance of its insights improve over time. this is often a type of machine learning process.

How Text Analytics Works

The text analytics process starts by gathering giant-size text data sets, counting on the scope of your project and the resources that you have available, you’ll pull from social media comments, website text, books, structured surveys, unplanned feedback, or call logs. Working with a single set of data or managing multiple aggregated resources. The text analytics solution may have text mining features found so that it begins to sort this data. In some cases, you’d use two or more solutions to generate the extracted data sets needed to find valuable information. Some samples of what happens during this part of the process include breaking down the sentence, tokenizing the text, and configuring the language.

The language processing feature in the software can manipulate the data in many ways, like tagging it, grouping it, or setting it up in other taxonomies. After the essential, low-level processing is completed, the text analytics tool can move to the subsequent step.

Often usage of this technology is to perform sentiment analysis on a given set of data. The software can discover whether customers are unhappy or happy, the topics that they feel hooked to, and notable input about the customer experiences. It parses the syntax and context of the text to work out the real message behind it. In the end, build on the text analytics’ interpretation of this information.

Types of Text Analytics Tools

You have various categories of text analytics solutions you can choose for your organization. The proper choice depends on how you want to use the solution, the kinds of specialists that you have available, your budget, and therefore the kind of text that you’re working with.

Bare Bones APIs: If you’ve got a strong development and data science team available and the need to build a fully customized solution, you’ll start with Bare Bones APIs that include the text analytics features required.

Text Mining and Coding: this sort of solution processes verbatim comments to extract the most critical information from them. It’s important to notice that this category doesn’t include analytics. It categorizes, sorts, and labels text, among other functions.

Text Analytics: Text analytics solutions specialize in surfacing insights from text, and allowing you to control the data as needed to understand it better.

Data Visualization: These solutions take the info from text analytics and present it in a variety of visualizations. This reporting tool can help non-technical users learn more about the info and use it to inform their decisions.

Collection of Tools: If you don’t want to piece multiple solutions together, all-in-one tools are available that include text mining, coding, analytics, visualizations, and other useful features. The comprehensive system may be a good choice for those starting in text analytics, also as those that have plenty of experience. A good system accommodates both of these, end-user groups to support business growth over a long-term period.

Text Analysis with Python

Python is the most popular programming language today, particularly in the field of scientific computing, as it is a highly intuitive language. It is briefer, so it takes less effort and time to carry out certain tasks. Ultimately, the code readability and syntax make it efficient, easy to process, and easy to learn. All these advantages make Python the perfect option to build a machine learning model for text analysis. You might opt for open-source libraries, such as NLTK or Scikit-learn. Still, building a text analysis model from scratch is not easy. You will need to spend more time and resources building the necessary infrastructure to run the model, train it, test it, and start all over again until it can be put to use.


Speech and text analytics enable you to gain insights into customer-agent conversations through sentiment analysis. These insights highlight areas of recognition, concern, and improvement to better understand and serve customers.

Speech Analytics is a collection of statistical algorithms and programs that help to analyze pre-recorded or live calls and gather structured data from an unstructured conversation. Speech analytics will use different voice parameters that reflect changes in the somatic and autonomic nervous system to detect different moods, emotions, and stress levels.

Text analytics uses words and phrases with positive/negative suggestions, as well as disfluency words to gauge emotion.

Capabilities of Text Analytics

Custom dashboards and reports: Your organization may have many types of data visualization, counting on the use case. The ability to customize dashboards and reports allows them to look at the data in many ways.

Automatic Translation: Companies with a worldwide presence, or those in multilingual markets, may have to translate comments. The system can detect the language being written and then translate it into a specified language.

API Connectors: Text analytics solutions with API connectors streamline the method for accessing data in other software.

Data Import and Export: Native integration with popular third-party solutions is useful for interoperability.

Data Scrubbing: Extract personally-identifying information, curse words, and other information from the text before it’s analyzed.

Real-time and Dynamic Results: You’ll act quickly to identify issues with your customer experience and other parts of your operation when you have real-time data available.

Granulated Analysis: Drill down into the insights that are most relevant to your needs and narrow through the signal to noise ratio.

Comprehensive Automation: A considerable part of text analytics solutions should be automated. The sheer volume of text data available for the standard business exceeds manual processing capabilities.

Benefits of Text Analytics

Text analytics delivers many advantages to your organization, as it’s a crucial part of extracting value from the unstructured data sets that you’re otherwise unable to process.

  1. Work with Verbatim Comments in many sorts of Media or Language – You are not constrained to a particular format or media type with text analytics. This compatibility cuts down on the quantity of pre-processing you need to do with your data before inputting it into the system.
  1. Improve Experiences for patrons, Employees, and Other Stakeholders – Your customer experience, employee engagement, and other areas will have to change needs over time. You’ll continually find ways to improve this by taking direct, verbatim feedback and discovering the trends within the data.
  1. Increase your Company’s Revenue – Happy customers persist with your company and make repeated purchases, while highly engaged employees are loyal and turnover rates go down. Once you can stay on top of the areas that are most important to these parties, you set your business in a position where it can enjoy sustained long-term growth.
  1. Gain Better Control Over your Costs – Your budgeting is informed by the text analytics data. The areas that require the greatest investment are targeted, while people who have less of an impact on operations have a lower priority.
  1. Boost Efficiency of Working with Unstructured Data – One of the biggest time-sinks of working with unstructured data is putting it in a form that standard analytics systems can work with. Text analytics can take the info as-is without conversion or other tedious manual tasks.
  1. Make More Data-driven Decisions – Valuable insights from text data and verbatim comments offer you another source of data for your decision-making process.
  1. Act Quickly on New Opportunities – Your market can change drastically overnight, often in response to new technology, innovations, or ongoing feedback. Text analytics gives you access to the present information so you can discover new opportunities that lend a competitive advantage. An example of working on this information is suggestions for use cases that you haven’t considered before. If customers speak about using your product in unexpected ways, you’ll create resources to support that use case, develop products specifically fitted to that market, or expand horizontally into a replacement market.

Few Use Cases for Text Analytics Software: Many companies have limited insights into their customer experience as they depend on surveys that lack open-ended questions. While these data sources are useful, they often lack information about why someone chose a specific response. Empowering your organization through open-ended questions exposes more communication channels between you and your customers. They will go into detail about their recent experiences and what they think about your company. Here are some use cases for text analytics software.

1- Researching the Voice of the Customer

2- Gaining Greater Understanding of Net Promoter Scores

3- Identifying Problem Areas in Customer Satisfaction Surveys

4- Getting Feedback on New Concepts

5- Discovering Employee Engagement Levels

6- Testing the Effectiveness of Advertising

How FutureAnalytica can help in this journey?

FutureAnalytica is the only holistic automated machine-learning, no-code Artificial Intelligence platform providing end-to-end seamless data-science functionality with data-lake, Artificial Intelligence app-store & world-class data-science support, thus reducing time and effort in your data-science and artificial intelligence journey.

We at FutureAnalytica help you to build tools that can summarize multiple properties in 2 to 3-word phrases. Understanding CXP based on factors valuable to improve products, and services.

With FutureAnalytica’s advanced solutions Customer communication data can be digested at scale and analyzed to find data-driven insights for customer service teams to outperform their KPIs. Automate and prioritize the appropriate representative based on the customer problem, and urgency of the ticket contents. Intent recognition and Spoken Language Understanding services for detecting user intents from short utterances can help to guide traders in deciding what to trade, how much, and how quickly other solutions like lead generation, recruitment, and review sites help businesses analyze huge quantities of text-based data in a scalable, consistent and unbiased manner.


By summarizing the results of text analysis and using advanced intelligence tools such as Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies enhance capabilities for quantitative insights so that there are many advantages of text analytics for businesses. It enables enterprises to uncover easy-to-understand reports and graphics, and text analytics can identify patterns, trends, and actionable insights in data to make smarter decisions.

After analyzing the content of customer support and customer feedback, these results can be leveraged using text analytics to help in detecting opportunities for improvement and adapting products or services as per the client’s needs and expectations. Confident, prompt decisions are made and not based on guesswork thus, sustaining the long-term growth of the business. This is the method of text analytics that is shaping the future of businesses across industries.

We hope this article was insightful and helped you to understand AI-based Text analytics and how it benefits and shapes the future of various industries. Thank you for showing interest in our blog and if you have any questions related to Text Analytics, Text Mining, Text Analysis, Sentiment Analysis, NLP, Machine Learning, or AI-based platforms, please send us an email at

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