Text Mining Explained: How To Do It Right & Act on Feedback

Still tagging open-ended feedback on spreadsheets?

That’s so 2000s! 

Get it automated!

Curious? Let’s scroll down!

What is Text Mining?

Text mining is the process of the analysis and making sense of thousands of pieces of feedback, uncovering sentiments, patterns, pain points, etc, in mere minutes. From spotting customer sentiment in reviews to detecting fraud in financial reports, text mining helps businesses turn unstructured text into actionable insights. It is referred to as the “data preparation” stage of text analysis.

Imagine a grocery store launching an online delivery app. To gauge customer satisfaction, they run a CSAT survey across multiple channels and receive 2,000+ open-ended responses. Manually analyzing them? Time-consuming and error-prone.
Instead of manually reading each review, they use text-mining tools to automatically analyze the data. The system identifies common themes – like “fast delivery” and “poor customer support”- and even determines sentiment, showing that while customers love the delivery speed, they are frustrated with support.

But is this process important?

Why is Text Mining important?

80% to 90% of business data is unstructured – hidden in emails, customer reviews, social media posts, support tickets, and more. In this case, traditional data analysis methods struggle to process this vast amount of text, which is where text mining becomes a game-changer.

Here’s why text mining is crucial across industries:

• Extracts Actionable Insights: Every day, around 328.77 million terabytes of data are created globally. Text mining helps organizations make sense of this overwhelming data by identifying patterns, relationships, and trends that would otherwise go unnoticed.
• Saves Time and Effort: Manually reading through thousands of documents or customer feedback is nearly impossible. Text mining automates this process, reducing analysis time from weeks to minutes. 
• Enhances Decision-Making: It helps businesses make data-driven decisions based on real customer feedback, social media trends, and more. And companies that leverage data analytics, are 23 times more likely to acquire customers and 6 times more likely to retain them.
• Improves Customer Experience: Text mining helps businesses track customer sentiment, and identifies pain points and preferences from reviews, support tickets, and surveys.
• Boosts Competitive Advantage: It helps companies track industry trends, monitor competitors, and understand market sentiment.

But doesn’t it sound similar to text analysis? 

Text Mining vs. Text Analytics – What’s the Difference?

Here’s how text mining and text analytics are different from each other.

Both text mining and text analysis deal with analyzing text and are often used interchangeably, and the distinction can be subtle.

Text Mining is about discovering hidden patterns within unstructured text data. It helps in data exploration by finding key entities, topics, and trends. Businesses use it for fraud detection, legal analysis, and cybersecurity to uncover critical insights that aren’t immediately visible.

Text Analysis, on the other hand, focuses on interpreting text data to derive meaningful conclusions. It uses sentiment analysis, opinion mining, and predictive analytics to understand customer feedback, market trends, and brand perception. For instance, companies use text analytics to monitor social media sentiment and adjust their marketing strategies accordingly.

While text mining is exploratory, text analytics is interpretative – together, they provide a powerful way to unlock insights from textual data!

Stop guessing what your customers think. Leverage SurveySensum to analyze thousands of responses in seconds and focus on what matters most!

Now let’s understand how text mining works.

How Does Text Mining Work?

The process of text mining involves steps like data collection, cleaning, etc – each step adds significant value and deep analysis for the overall process. Let’s now discuss each step.

1. Data Collection

Gather customer feedback from a wide range of sources, including in-app feedback, chat conversations, Play Store reviews, emails, surveys, and social media, to gain a comprehensive understanding of customer sentiment. This will ensure that you’re listening to the voice of the customers on all channels, not just one.

SurveySensum creates a consolidated and unified view of customer data. Instead of siloed insights from different channels, you get a holistic understanding of customer sentiment, pain points, and customer expectations in one place.

2. Text Preprocessing

Raw text data is messy, and filled with irrelevant words, typos, and inconsistencies. Cleaning the data ensures accuracy and efficiency in analysis. This step includes:

• Word Spotting: It is a method of pinpointing specific words that embody the essential meaning of a sentence. This technique operates under the premise that the presence of a particular word indicates that the entire sentence is centered around that word.

In the example image, SurveySensum identifies the word “experience” in customer feedback and categorizes the sentence as relating to overall customer experience. Similarly, the phrase “loved it” is interpreted as indicating a positive overall experience with the airline.

• Tokenization: It is the process of breaking down text into smaller units, such as words or phrases, to analyze the meaning of each word and its relationship to other words.

For example, with SurveySensum’s text analytics software, unstructured feedback is broken down into smaller relevant units to analyze each word’s meaning and overall emotion.

• Chunking: It is the process of grouping words into meaningful units, or “chunks,” based on their grammatical categories (such as noun phrases, verb phrases, and adjective phrases).

For example, the sentence “Staff (Ground and On-board)” is ambiguous and difficult to interpret. However, using text analytics, it can be broken down into more meaningful and easily understood phrases, such as “good service by cabin crew” and “good ground services.” This process, called chunking, helps the system understand the relationships between different parts of speech and improves the accuracy of text interpretation.

3. Understanding the Context With NLP

Once the text is cleaned, Natural Language Processing (NLP) techniques help extract meaning from it. Some key NLP techniques used in text mining include:

• Named Entity Recognition (NER): Identifies people, locations, and brands.
• Sentiment Analysis: Determines whether the text expresses positive, negative, or neutral sentiment.
• Topic Modeling: Detects key themes and patterns across large text datasets.
• Text Classification: Categorizes text into predefined groups (e.g., spam vs. non-spam emails).

SurveySensum’s AI-powered text mining software automatically categorizes feedback by assigning tags and subtags based on identified keywords and themes. The AI models can be trained to improve accuracy and customize categorization to align with specific business requirements.
The AI analyzes up to 10,000 open-ended responses in just 5 seconds with 99% accuracy, allowing businesses to quickly identify key trends and sentiments without manual effort.

Customers are telling you exactly what they want – but can you keep up? SurveySensum’s AI-driven text mining scans and categorizes responses instantly so you can act faster and smarter!

4. Data Visualization

The results from text mining are commonly displayed in reports or dashboards to give businesses actionable insights. These visualizations simplify complex text data and enable decision-makers to quickly interpret and act on the information, which may include sentiment trends, frequently mentioned topics, customer pain points, and emerging market trends.

With SurveySensum’s advanced data visualization, you can gain AI-powered insights that pinpoint the key drivers of customer sentiment, allowing you to take targeted and prioritized actions that yield the most impact on your bottom line.

By following these steps, text mining helps you to identify customer pain points and improve products, monitor brand reputation, and track competitor strategies. 

With SurveySensum, you can analyze customer feedback in seconds, identify key trends, and make data-driven improvements!

Let’s now talk about its key applications.

Real Application of Text Mining

Given the various approaches and benefits of text mining, how is text mining applied in business? Here are some of the business applications of text analytics.

1. Customer Experience

By analyzing customer feedback from surveys, reviews, and support tickets, businesses can identify common pain points and areas for improvement. For instance, text mining can reveal recurring complaints about product quality or service delays, enabling companies to address these issues proactively.​

This helps in

• Churn Prediction: Proactively identify and address customer dissatisfaction by monitoring customer interactions for negative sentiment trends. For example, if a customer expresses thoughts of canceling their service, trigger proactive retention efforts to prevent customer churn.
• Cross-sell/Up sell: Opportunities for cross-selling and upselling can be found by analyzing operational data such as CLV and customer spending in conjunction with renewal dates and topics like rewards and incentives.

2. Customer Service

Text mining can assist customer support teams to retain customers and guarantee a positive brand experience.

• By categorizing and prioritizing customer inquiries, support teams respond more efficiently.
• By analyzing post-interaction surveys and customer complaints, you can evaluate agent performance and identify areas for improvement.

SurveySensum enables your teams to track customer trends and respond quickly to dissatisfaction by instantly flagging and escalating detractor feedback to the appropriate team with its real-time ticketing system. Further, you can track ticket trends to measure resolution time and open ticket volume.

3. Social Media Monitoring

Monitoring social media platforms through text mining enables companies to gauge public sentiment about their brand, products, or services. This real-time analysis helps in identifying potential crises, understanding customer opinions, and tailoring marketing strategies accordingly.

SurveySensum integrates social media text mining, helping you track real-time brand perception. If a product launch or campaign generates negative sentiment, the system alerts brands so your team can act quickly to address concerns and protect your reputation.

4. Market Research

Text mining facilitates the analysis of large volumes of market data, such as industry reports, news articles, and competitor information. 

This helps in

• Customer Insights: Businesses can leverage text mining to extract valuable information about customer preferences and behaviors from various sources, including search queries, reviews, and social media posts. These insights enable businesses to create targeted advertising campaigns that effectively resonate with specific customer segments.
• Real-Time Trend Analysis: Businesses can monitor popular topics and keywords to modify their advertising campaigns and better reflect current customer interests. If “sustainability” is a hot topic, for instance, they might give priority to advertisements that emphasize environmentally friendly business practices.

Customer expectations are ever-evolving – are you keeping up? With SurveySensum’s MX platform, launch expert-designed NPS surveys and get real-time feedback in just 24 hours!

5. Product Development

Product managers can leverage customer feedback, trend monitoring, and competitor analysis to develop enhanced campaigns and products. 

Text mining helps in

• Analyzing online reviews, social media conversations, survey responses, etc to understand customers’ perceptions of new product launches and features. 
• Helping analyze customer feedback in real-time, detecting new trends before competitors do. This allows you to stay ahead of market shifts, ensuring that your marketing strategies align with customer interests.
• By analyzing discussions about competitors, you can understand strengths and weaknesses in the market.

With SurveySensum you can empower your product teams by providing them with deep sentiment insights, allowing you to fine-tune your offerings, highlight winning product features, and craft campaigns that resonate with the target audience. By integrating real-time sentiment tracking, you can quickly adjust product development strategies to maintain a positive brand image.

Wrapping Up!

In today’s data-driven world, text mining isn’t just a luxury – it’s a necessity. Businesses that leverage text mining gain a strategic advantage from understanding customer sentiment to predicting market trends.

But here’s the thing: raw data alone isn’t enough – you need the right tools to extract real value from it. That’s where SurveySensum comes in! With AI-powered text mining, SurveySensum helps businesses analyze open-ended survey responses, detect sentiment, and uncover key trends – all in real-time.