Text Mining: Unlocking Insights from Feedback Text Data

Your customers are telling you everything — but it’s buried in thousands of words.

Surveys. Reviews. Chat logs. Support tickets. NPS verbatims. Every day, teams are flooded with raw feedback that holds real insight into what’s working, what’s broken, and what to fix next. But without a way to process it at scale, most of that qualitative insights gets ignored, lost in spreadsheets, or manually scanned weeks too late to act.

That’s where text mining changes the game.

Text mining uses natural language processing (NLP) and machine learning to analyze open-text responses automatically surfacing key themes, sentiments, intent, and patterns. It transforms scattered feedback into structured intelligence that CX, Product, and Marketing teams can actually use to improve experiences, drive loyalty, and stay ahead of churn.

This article dives into text mining, a process that leverages algorithms to extract actionable insights from texts such as customer feedback, social media posts, reviews, and documents. We will discover the methods used, the benefits for businesses, and real-world examples of text mining in action.

TL;DR

• Text mining extracts insights from unstructured text data like customer feedback through methods like natural language processing.
• It involves steps such as data preparation, cleaning, and extracting information using techniques like sentiment analysis. 
• Businesses utilize text mining to improve CX, drives product decisions, optimizes messaging, and aligns teams — by revealing what customers care about and where friction hides.
• From sentiment analysis and topic modeling to intent and entity recognition — each technique surfaces different angles of customer voice for better clarity and prioritization.
• Whether it’s routing critical support issues, decoding NPS drops, or spotting product friction, text mining helps prioritize what to fix — and what to amplify.
• Zonka Feedback is a customer experience software purpose-built to turn unstructured feedback into structured, decision-ready insight. Its AI Feedback Intelligence engine uses advanced text mining techniques including sentiment analysis, entity recognition, and theme detection to help teams analyze at scale, act instantly, and close the loop. You can schedule a demo or start your free trial to explore how it powers feedback-led growth. 

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What is Text Mining? 

Text mining is the process of turning unstructured text like open-ended question responses, chat logs, reviews, and support tickets — into structured, actionable insights. It uses natural language processing (NLP) and machine learning to automatically identify key themes, sentiment, and intent across large volumes of feedback.

Unlike traditional data analysis, which depends on numbers and structured fields, text mining focuses on the human language and gives your team the ability to understand at scale what customers are really saying and feeling. If you’re collecting feedback from multiple touchpoints, you already know how overwhelming it can be to go through every comment manually. What’s worse? The most valuable insights often hide in that unstructured, open-text data — and get missed altogether.

With text mining, you don’t have to guess what customers care about. You can spot recurring issues, uncover emotional drivers, and translate raw text into trends you can act on — all in real time. Instead of reading 500 reviews or survey comments line by line, text mining shows you what they’re really saying, what’s changing, and what to prioritize next.

Understanding Unstructured Data

Unstructured data is characterized by its lack of organization or predefined format. It can take many forms, including news stories, job listings, movie reviews and social media posts. Despite being difficult to manage initially, unstructured text data holds great value for businesses as it allows them to uncover patterns in customer behavior and gain valuable insights.

The challenge lies in converting this unstructured text into a structured format that enables effective analysis. This transformation process is essential as it ultimately unlocks the potential of unstructured data for organizations.

In customer feedback, unstructured data could be in different formats. 

• Open-ended questions: These invite respondents to answer in their own words, providing valuable insights beyond fixed options. Examples include:
  • "What did you dislike most about the product?"
  • "What suggestions do you have for improvement?"
  • "Please describe your overall experience with our service."
• Comments and feedback boxes: While these may often focus on specific questions, they leave room for additional thoughts and feedback not captured by other response options.
• Text responses to rating scales: While the rating itself might be structured (e.g., 1-5 raing scale, 5-stars, Likert Scale), the accompanying text can offer valuable context and reasoning behind the rating.
• Images and multimedia: Some surveys might collect pictures, audio recordings, or videos, requiring specific tools for analysis beyond traditional text analysis.
• Social media conversations: Surveys embedded in social media platforms might capture unstructured responses and comments alongside structured data like demographics.

💡Did you know? Over 80% of customer feedback is unstructured — and most teams don’t analyze it at all. That’s hundreds (or thousands) of missed signals, every single month. Without the ability to structure and analyze this kind of data at scale, valuable patterns get buried. But with text mining, these fragments become signals — helping you understand not just what happened, but why.

The Role of NLP (Natural Language Processing) in Text Mining

At the heart of text mining is Natural Language Processing (NLP) — the technology that allows machines to understand and interpret human language. NLP turns raw, unstructured text into structured data by identifying words, relationships, sentiment, entities, and topics, making it possible to analyze thousands of comments, reviews, or chat transcripts in seconds.

Here’s what NLP does in a text mining system:

• Understands context, not just keywords distinguishing between “cancelled my subscription” and “thinking of cancelling”
• Identifies sentiment and emotion so you know how customers feel, not just what they’re saying
• Detects intent uncovering whether feedback is a suggestion, complaint, question, or praise
• Extracts entities and key themes like specific product names, locations, features, or experiences
• Summarizes long-form feedback turning paragraphs into quick, scannable insights for action

By combining NLP with machine learning, you can move beyond tagging and reporting — and actually start understanding what drives churn, what’s working, and what needs to change.

Benefits of Text Mining: Powering Businesses

Now that the basics of text mining have been understood, it is important to delve into its potential benefits for businesses. Through text mining, you can act on unstructured data faster, more intelligently, and with greater precision.

By analyzing feedback across channels in real time, text mining helps you identify what matters most to customers, where experience is breaking down, and how sentiment is shifting, without waiting weeks to read every comment manually.

Here’s how that translates into real value:

1. Deeper Customer Understanding and Improved Experience

Text mining allows businesses to uncover hidden emotions, nuances, pain points, and issues behind customer feedback. It gives them valuable insights and helps them understand what customers are actually feeling — and how that impacts their experience thereby helping you improve customer satisfaction. 

• Beyond surface-level responses: Go beyond simple ratings or "yes/no" answers to uncover true motivations, preferences, and pain points.
• Identify hidden trends and patterns: Discover common themes and issues raised across various feedback channels, providing a holistic view of customer sentiment.
• Proactive and personalized service: Anticipate needs, resolve issues faster, and improve support before problems escalate.
• Measure sentiment over time: Track customer satisfaction trends and detect early signals of dissatisfaction.
• Segment and personalize experiences: Analyze feedback based on demographics, purchase history, or other factors to tailor products, services, and marketing to specific customer segments. 

2. Improved Products and Services

Customer feedback often contains valuable suggestions for product improvements or new features. Text mining allows businesses to systematically analyze this feedback and prioritize product enhancements based on customer demand, leading to more competitive and customer-centric offerings.

• Identify product flaws and improvement opportunities: Understand what customers love and hate about your products, informing future iterations and development.
• Proactive issue detection and resolution: Uncover emerging problems before they escalate, addressing them swiftly and preventing customer churn.
• Prioritize development efforts: Base product roadmap decisions on actual customer feedback and needs, ensuring resources are allocated effectively.

3. Enhanced Marketing and Sales

Text mining enables businesses to better align marketing and sales strategies with customer expectations. By analyzing feedback, businesses can identify opportunities, optimize messaging, and refine campaign direction — all grounded in real customer voice.

• Targeted campaigns and personalization: Analyze feedback to understand customer preferences and tailor marketing messages and offers for higher engagement and conversion rates.
• Discover effective messaging and language: Learn what resonates with customers and use that knowledge to craft compelling marketing materials and sales pitches.
• Identify key influencers and brand advocates: Find customers who consistently rave about your products and leverage their testimonials for social proof and word-of-mouth marketing.

4. Cross-Functional Alignment Through Unified Insights

When feedback lives in silos, it’s harder to connect the dots. Text mining brings all voice-of-customer data into one place — giving teams a shared understanding of what’s driving customer satisfaction or frustration.

• Enable smarter collaboration: Let product, support, marketing, and operations work from the same insights.
• Create accountability with shared dashboards: Track trends, themes, and sentiment changes across teams.
• Turn feedback into a shared decision-making input: Break silos and align teams around what matters most to customers.

5. Accelerated Insight & Experience Risk Mitigation

One of the most powerful advantages of text mining is how quickly it transforms raw feedback into insight — ready to share, act on, or escalate. Instead of spending days manually reviewing comments, teams get real-time access to patterns, sentiment shifts, and recurring issues.

• Faster time to insight: Use auto-generated summaries, trend graphs, and dashboards to spot what’s changing without waiting on deep-dive reports.
• Executive-ready reporting: Convert qualitative feedback into presentation-ready insights for stakeholders and leadership updates.
• Catch risks before they escalate: Detect early signals of churn, service breakdowns, or negative sentiment spikes so issues can be resolved before they impact satisfaction or reputation.
• Strengthen trust and agility: Respond quickly and transparently to customer concerns using real-time, theme-based alerts and data.

Text Mining vs Other Data Approaches: What You Need to Know

After understanding what text mining can unlock, it’s natural to ask: How does it compare to other methods? There’s often confusion between text mining, text analytics, and data mining — especially when all three involve “finding insights.”

But not all approaches are designed for open-ended customer feedback — or the speed and nuance your teams need to respond to it. Here’s a closer look at how these methods differ, and where text mining uniquely shines for feedback-led teams.

Text Mining and Text Analytics: A Comparative View

Text mining tells you what’s showing up across customer feedback — recurring issues, emotional spikes, or trending themes. Text analytics goes a step further: it helps you explore why those patterns exist, and how they evolve over time.

When to use text analytics instead of (or after) mining:

• You’re running brand perception studies or longitudinal analysis
• You need to correlate feedback themes with external events, churn, or campaign performance
• You're creating presentations or insights for executive teams

For instance, a VoC leader sees a recurring dip in NPS. Mining revealed “late delivery” as a key complaint. Analytics then links that sentiment to seasonal peaks in certain regions — guiding ops improvements for the next quarter.

Data Mining: Structured Behavior vs Human Voice

Data mining isn’t designed for language. It uncovers patterns in structured datasets — think product usage, revenue logs, CRM activity. It works best for discovering behavioral trends and making predictive models.

When to use data mining instead of (or alongside) mining:

• You want to model purchase likelihood or upsell probability
• You're analyzing numerical trends (e.g. cart abandonment)
• You're segmenting customers based on behavior, not feedback

For instance, A product team mines survey comments to discover users are confused by onboarding. A data analyst complements this by using data mining to show these same users also have higher churn risk — linking sentiment with behavior.

Text Mining Techniques & Process: From Raw Data to Insights

Before feedback can fuel better decisions, it needs to be decoded. This section breaks down the key techniques and step-by-step process that power effective text mining — from identifying emotions and themes to transforming scattered responses into structured insights. Whether you're analyzing survey comments, support tickets, or reviews, this is how raw feedback becomes real intelligence.

Key Text Mining Techniques that Power Feedback Intelligence

Let us look at some text mining techniques that modern teams rely on to extract real customer intelligence from open-ended text.

1. Sentiment Analysis

When hundreds — or thousands — of customers are telling you how they feel, sentiment analysis helps you quantify emotion and urgency in real time. Whether it’s a glowing review or a subtle signal of frustration, this technique ensures nothing emotional slips through the cracks. Sentiment analysis automatically detects whether feedback is positive, negative, or neutral. But more importantly, it helps you detect when sentiment is shifting — and why.

In practice:

• A sudden dip in NPS comment sentiment after a product release helps Product Heads investigate usability issues before churn follows.
• A rise in positive sentiment in feedback about staff at one location signals best practices that can be replicated across branches.
• A marketing team uses sentiment charts to validate the emotional response to a new campaign rollout.

2. Entity Recognition

Feedback without context is noise. Entity recognition adds that missing layer by identifying names, places, features, teams — anything that matters. Instead of reading line by line to figure out what feature or location a customer is referring to, AI tags every mention automatically. You immediately know who or what they’re talking about.

In practice:

• A VoC team tracks complaints tagged with “payment failure” and isolates them to one region — helping Operations intervene faster.
• A Product Manager filters feedback on a feature like “search bar” to understand how its usability is impacting engagement.
• CX leaders compare entity-linked sentiment across stores, products, or service lines.

3. Topic Modeling

It’s one thing to read feedback. It’s another to instantly see what’s being talked about most, without lifting a finger. Topic modeling uses unsupervised learning to group feedback into high-level themes, like “billing,” “checkout delays,” or “agent behavior.” It's particularly useful when you don’t yet know what to look for — the model finds recurring topics for you.

Real-world impact:

• A CEO sees “payment portal issues” emerging as a rising theme post-launch — before support tickets spike.
• A VoC program surfaces “KYC process delays” across open-text survey comments, helping compliance and onboarding teams align.
• A retail brand learns that “trial room cleanliness” is showing up in reviews — not previously on anyone’s radar.

4. Intent Analysis

Not all feedback is created equal. Some customers want to leave, some are just confused, and others are offering ideas you’d be crazy to ignore.

Intent analysis decodes the ‘why’ behind the words — tagging feedback as a complaint, request, query, suggestion, or praise. When paired with urgency or sentiment, it helps you know not just what to respond to, but what to prioritize.

Use it to:

• Route “I might switch providers” to the Retention team instantly
• Flag “Feature request: dark mode” to Product with one click
• Separate routine inquiries from escalations — before they hit support volume thresholds

5. Text Classification

Feedback doesn’t become usable until it’s organized. Classification allows you to assign predefined tags or categories to incoming text automatically. This is especially powerful for teams that rely on consistent tagging, filtering, and reporting — without spending hours doing it manually.

For instance:

• A CX team classifies survey comments into “product,” “service,” and “pricing” — helping each department get only what’s relevant.
• A VoC dashboard sorts feedback into “Bug report,” “Suggestion,” and “Praise” — keeping leadership summaries crisp and actionable.
• Marketing can isolate “brand perception” mentions to analyze emotional alignment with recent messaging.
• Text classification is how feedback becomes a clean, structured dataset — not just a folder of loose words.

Text Mining Process — From Feedback to Strategic Insight

Now that we’ve explored the techniques, let’s walk through the end-to-end process that brings them to life — from raw feedback to action-ready intelligence.

Step 1. Data Gathering: The first step in text mining is to collect customer feedback from various sources such as surveys, reviews, social media comments, and support tickets. This allows businesses to gather a diverse range of opinions and perspectives.

Step 2. Data Cleaning & Prepping: Once the data is gathered, it needs to be cleaned and prepped for analysis. This involves removing irrelevant information and standardizing formats. By doing so, businesses can ensure that the data is accurate and can be effectively analyzed.

Step 3. Text Processing: After cleaning the data, the next step is to break down the text into manageable chunks such as words and phrases. This includes tokenization, stemming/lemmatization, and part-of-speech tagging. Additionally, tagging these chunks with their grammatical roles, such as nouns and verbs, can provide further context and insights.

Step 4. Extracting Information: The next step after preparing and cleaning the training data is to extract information using text mining methods. These techniques, which include natural language processing (NLP), information extraction, and information retrieval, are used to identify and extract important entities and concepts from textual data.

These enable computers to understand human language better and uncover insights such as sentiment analysis, summarization, and aspect mining. This process makes use of NLP tools that facilitate comprehension of natural language for tasks like retrieving relevant information from a given text. 

Step 5. Feature Extraction: Identifying key elements within the text is crucial for deriving meaningful insights. This includes extracting information such as word frequency, sentiment (positive, negative, neutral), and specific topics discussed. By analyzing these features, businesses can understand the overall sentiment and prevalent themes within the feedback data.

Step 5. Analysis & Modeling: To uncover trends and patterns within the feedback data, businesses can utilize various text mining tools and techniques. Sentiment analysis can help identify the overall sentiment of the feedback, topic modeling can group similar comments together, opinion mining can identify opinions behind the text, intent analysis can tell the basic intention and entity recognition can identify specific entities mentioned.

These analyses provide a deeper understanding of the feedback data and highlight important patterns.

Step 6. Insights & Action: The final step in the text mining process is to translate the findings into actionable steps. These insights can be used to drive product improvements, create targeted marketing campaigns, and enhance personalized customer service interactions. By taking action based on the insights gained from text mining, businesses can improve customer satisfaction and mitigate potential risks.

By following these steps, you can effectively analyze and derive valuable insights from raw feedback data. Text mining enables businesses to unlock the potential within unstructured textual information and make data-driven decisions to improve customer satisfaction and support risk management.

Text Mining in Action: Applications & Use Cases

Text mining turns raw, unstructured feedback into structured intelligence — helping teams surface what matters, prioritize improvements, and close the loop faster. Here’s how it’s used in practice across different feedback channels and business needs:

1. Analyzing Open-Ended Survey Responses at Scale

Surveys often include comment boxes that reveal far more than score-based ratings. Text mining helps extract insights from open-text responses without manually reviewing each one.

For instance, after a feature release, survey responses pour in. Text mining instantly surfaces themes like “confusing UI” and “missing instructions,” categorizing feedback by sentiment and tagging recurring issues — helping the team act before negative reviews escalate.

2. Improving Customer Experience with Real-Time Feedback Classification

Support tickets, reviews, and chat logs contain valuable cues about satisfaction, frustration, and urgency. Text mining helps detect these signals early — and respond quickly.

For instance, incoming support messages are auto-tagged by topic and urgency. Issues like “payment failure” or “long wait time” are flagged and routed to the right teams, reducing resolution delays and improving customer experience metrics.

3. Identifying Friction Points in the Customer Journey

By analyzing feedback across different touchpoints — onboarding, post-purchase, or renewal — teams can pinpoint where satisfaction drops and what’s causing it.

For instance, a dip in NPS triggers a deep-dive into feedback from the onboarding stage. Text mining reveals that “KYC delays” and “lack of guidance” are the dominant pain points — enabling workflow updates that reduce churn.

4. Prioritizing Product Improvements from User Feedback

Product teams receive feedback from multiple channels — surveys, app store reviews, user forums. Text mining organizes this input into actionable themes that guide roadmap decisions.

For instance, instead of guessing what to fix next, teams can filter comments by product area (e.g., “dashboard” or “reporting”), sentiment, and frequency — making it easy to prioritize high-impact improvements.

5. Monitoring Brand & Campaign Sentiment

Text mining applied to social comments, reviews, and post-campaign surveys helps uncover how customers feel — and why. It goes beyond volume metrics to identify emotional signals and reputational shifts.

For instance, after a new pricing strategy is announced, teams track the shift in sentiment across reviews and social mentions. Text mining detects phrases like “not worth it” or “transparent pricing” and links them to campaign reactions — enabling mid-cycle messaging adjustments.

6. Enabling Better Reporting with Structured Insights

Raw feedback isn't presentation-ready. Text mining translates it into structured datasets — sorted by themes, intent, urgency, and sentiment — ready for dashboards or stakeholder updates.

For instance, leadership dashboards now include a real-time feed of “top 5 negative themes this week” across all customer channels, supported by sentiment scores and volume. Insights become instantly accessible — not buried in a spreadsheet.

Selecting the Right Text Mining Tools and Technologies

Choosing the right tools and technologies is crucial for leveraging the advantages of text mining. It can be challenging to determine which text mining software is best suited for your needs. Some important aspects to consider include user-friendliness, scalability, and compatibility with current systems. 

Here is how you can choose the tool for mining text data:

• Define Your Requirements: Start by clearly defining your text mining objectives and requirements. 
• Research Available Tools: Conduct thorough research to identify text mining tools that meet your requirements. Consider factors such as functionality, ease of use, scalability, performance, data compatibility, security, and cost. 
• Evaluate Text Mining Algorithms & Models: Different tools have different text mining capabilities. Some widely used algorithms and models for text mining include decision trees, clustering algorithms, and neural networks.
• Check Compatibility and Integration: Determine whether the text mining tool is compatible with your existing systems, databases, and text data formats.
• Consider Customization and Flexibility: Choose a text mining tool that allows for customization and flexibility to adapt to your specific use case and requirements.
• Experiment & Evaluate: Test the functionality of the tools by taking free trials or demos.  

Best Practices for Using Text Mining

Text mining is most powerful when it moves beyond basic data extraction and becomes part of a systematic insight workflow. When applied thoughtfully, it enables faster decisions, sharper prioritization, and greater alignment across functions.

Here’s how to ensure your text mining strategy delivers lasting value:

1. Automate the Basics — So You Can Focus on Decisions

Manual tagging, sentiment labeling, and feedback categorization are time-consuming and error-prone. Automating these steps using AI not only saves time, but also ensures consistent, unbiased analysis at scale.

You need to set up automated pipelines to classify sentiment, detect key entities, and group feedback by topic — across all sources like surveys, reviews, and support logs. Use smart filters or dashboards to surface what matters most, without sifting through raw input.

2. Align Mining Techniques to Business Outcomes

Not every technique fits every goal. Choose your text mining methods based on the insights you want to surface — whether that’s predicting churn, prioritizing product issues, or detecting shifts in satisfaction.

You can use sentiment analysis to monitor emotional trends, topic modeling to group recurring pain points, and intent detection to identify high-risk signals like dissatisfaction or churn. Always tie insights back to impact areas like NPS, CSAT, or conversion.

3. Integrate Text Mining into Daily Workflows

Insights are only as valuable as their adoption. To maximize the impact of text mining, feed its outputs directly into existing tools, reports, and decision frameworks.

You should route categorized feedback to the relevant teams automatically — product, support, or marketing. Use alerts for themes tagged as urgent. Add summary dashboards into existing review meetings or performance tracking systems.

4. Combine Structured & Unstructured Insights

Text mining is strongest when paired with behavioral or operational data. Together, they show what customers are saying alongside what they’re doing — giving you a full-picture view.

Ensure to compare sentiment trends with usage data, retention scores, or revenue performance. For example, rising frustration themes paired with declining feature engagement can validate where product experience is failing.

5. Don’t Just Observe — Act on Patterns Early

It’s easy to analyze feedback in hindsight. The real value of text mining is spotting emerging themes before they escalate into lost revenue or churn.

Monitor early signals — like rising mentions of “confusing process” or “still waiting” — to trigger immediate follow-ups or pre-emptive interventions. Use thresholds to detect pattern spikes and automate escalation workflows.

Intelligent Text Mining & Beyond with Zonka Feedback

Text mining doesn’t just end at extraction — it begins a cycle of deeper understanding, contextual relevance, and automated action. Once you’ve structured your feedback, the next challenge is activating that insight across your organization. That’s where Zonka Feedback’s AI Feedback Intelligence elevates the process.

Built specifically for real-time, high-volume feedback environments, Zonka goes beyond standard text mining to deliver context-aware insights, auto-triggered workflows, and visual reporting — all in one powerful system.

Here’s how Zonka Feedback puts AI-powered text mining into action:

• Context-Aware Sentiment Analysis: Not all sentiment is obvious. Zonka Feedback uses context-aware algorithms to go beyond just identifying positive or negative tone — it picks up on subtle cues, emotional undertones, and shifting intensity in customer language. This allows you to detect dissatisfaction even when it’s wrapped in polite phrasing.
• Themes & Topic Classification: Manually tagging responses for recurring topics isn’t scalable. Zonka’s AI auto-classifies incoming feedback into themes and sub-themes, like “Delayed Delivery” or “App Navigation,” so you know what’s trending — and why. This helps you stay ahead of issues and spot opportunities at a glance.
• AI Smart Summaries: Sifting through long responses is time-consuming. Its AI Smart Summaries condense open-ended feedback into clear, concise overviews, capturing key sentiment, intent, and themes in just a few words. Teams can quickly scan what matters without reading every word.
• Entity Recognition: To act on feedback, you need specifics. Zonka Feedback detects and tags named entities like product names, locations, or team mentions within text — turning vague comments into actionable detail. A statement like “frustrated with login” becomes tied to “Mobile App v2.1,” sentiment-tagged and urgency-rated.
• Custom Attribute-Based Analysis: Zonka Feedback lets you filter feedback based on business-specific attributes — plan type, usage tier, location, or lifecycle stage. This gives you deeper insights into how different segments are experiencing your product or service, so you can tailor your response.
• Workflow Automation from Text Triggers: Text analysis doesn’t just inform — it activates. Zonka turns themes, urgency, and intent into automated actions. A frustrated cancellation mention? Automatically route it to retention. A product bug report? Trigger a support task with all relevant tags already applied.
• Visual Dashboards & Trend Reporting: All insights are visualized in real-time dashboards — showing shifts in sentiment, trending topics, and feedback volumes over time. These dashboards are built to be shared, filterable, and tailored to what different teams need to know — right when they need to know it.

Conclusion

Text mining is a highly effective technique for converting unstructured text data into meaningful information. Its various applications span from enhancing customer satisfaction to revolutionizing the healthcare industry. It has become an indispensable asset in the field of data analysis and with proper selection of tools and utilization of automation, businesses can optimize their operations, gain a competitive edge, and make well-informed decisions based on valuable insights obtained through text mining. The potential uses for this technology are boundless.

Zonka Feedback offers powerful artificial intelligence, machine learning, and natural language understanding capabilities. It can be used as a complete solution for collecting customer feedback through surveys, polls, quizzes, and feedback forms. The feedback can then be analyzed to derive insights and make data-driven decisions.

Its text and sentiment analysis tools allow businesses to uncover hidden insights behind unstructured data received from text. Apart from text and sentiment analysis, the survey tool offers intent analysis, emotion detection, opinion mining, and other text mining algorithms to help businesses in extracting information and making the most of data.   

Frequently Asked Questions

1. What is text mining with example?

Unstructured text is widely present in business data, including customer feedback, online reviews, survey responses and emails. It entails the process of extracting valuable information from unorganized pieces of text, which could be identifying features in product descriptions or company names within a dataset. This type of data can provide important insights for businesses to analyze data and utilize for decision-making purposes.

2. Is it hard to learn text mining?

The concept of text mining, which falls under the umbrella of natural language processing, can seem intimidating initially. By breaking it down into fundamental principles and methods, it becomes easier to grasp and learn.

3. What is NLP and text mining?

Natural language processing, also known as NLP, is dedicated to comprehending the underlying meaning and organization of written text in order to improve translation accuracy across languages.

In contrast, text mining involves analyzing word connections, occurrences, and arrangements within a given body of writing. Both methods provide valuable insights into natural language but have distinct focuses and goals.

4. How is text mining typically done?

The process of text mining involves utilizing natural language processing (NLP) and artificial intelligence (AI) methods to automatically analyze inputted text, identify patterns, and organize it for evaluation and interpretation.

This presents both advantages and difficulties for companies dealing with vast amounts of data.

5. What is the difference between qualitative and quantitative data?

Qualitative and quantitative data differ in the sense that qualitative data depicts individual attributes and subjective reasoning, while quantitative data comprises numerical values that indicate the amount or quantity.