According to statistics, approximately 328.77 million terabytes of data are created every day. All businesses are now surrounded by data generated from customer feedback, online reviews, social media interactions, and more.
Companies are investing heavily in collecting this data, but it is of no use if they cannot extract meaningful insights from it. To make the most of this data, businesses are turning to text mining.
But what is text mining, and how can it be used in your business? Here, we will explore everything you need to know about text mining, its techniques, and its applications in the industry.
Text Mining Definition
Text mining is the process of extracting insights and meaningful information from unstructured textual data. The goal is to explore and analyze large amounts of text data to identify patterns, themes, and relationships. Traditionally, this process relied heavily on these techniques, establishing a canon of practices over the years. Methods like topic modeling or popular word embeddings like, e.g. word2vec, were once central to text mining practices. However, in recent years, particularly from 2021 onwards, the landscape has significantly shifted, evolving rapidly from month to month. This transformation has been driven by the emergence of LLMs (Large Language Models).
Is Text Mining the Same as Text Analytics?
Both terms are often used interchangeably, but there is a slight difference between text mining and text analytics.
Text analytics is a broader term that encompasses all techniques used to analyze text data, including text mining. Text mining, on the other hand, focuses explicitly on extracting insights and information from unstructured text data. Both terms refer to the same process, but text mining is a more specific and precise term.
How Does Text Mining Work?
Text mining involves various steps that help transform unstructured data into structured data for analysis. These steps include:
Data collection
The first step in text data mining is data collection. As the name suggests, it involves gathering raw data from online platforms, documents, emails, social media, and other sources. The data formats can be text files, PDFs, CSVs, HTML, or even audio and video files. This data is then stored in a database for further processing.
Preprocessing
The next step is preprocessing, where the collected data is cleaned and standardized. This involves removing irrelevant characters, punctuation, and symbols from the data. It also involves handling misspellings, typos, and grammatical errors to ensure accurate text analysis.
Tokenization
Tokenization is the process of breaking down a sentence or paragraph into smaller units, such as words or phrases. Each word or phrase is called a token and is the building block for further analysis. Tokenization helps standardize text data and makes it easier to analyze.
Normalization
Normalization is the process of standardizing text by converting various forms of words into a consistent format, thereby reducing redundancy and improving analysis accuracy. This involves tasks such as handling strange characters, removing punctuation, converting text to lowercase, and dealing with whitespace inconsistencies. Normalizing text ensures that spelling, case, and formatting variations do not interfere with subsequent analyses or processing steps.
Feature extraction
Feature extraction reduces the dimensionality of the data by selecting relevant features or keywords from the text. Relevant features or attributes are extracted from the text and converted into numerical values for analysis.
Modeling and analysis
This step involves analyzing the text data using statistical models and machine learning algorithms. These models help identify patterns, trends, sentiments, and other important insights.
Evaluation and iteration
Evaluation and iteration involve analyzing the previous step's results, making necessary adjustments to improve accuracy, and repeating the analysis until satisfactory results are achieved.
Visualization and interpretation
The final step is visualizing and interpreting the results. Text mining vs NLP techniques, such as sentiment analysis, topic modeling, and text classification, extract meaningful insights from the data. These insights are then visualized in charts, graphs, or word clouds, making interpreting and understanding the data easier.
Text Mining Methods and Techniques
Text mining techniques help enhance the accuracy and effectiveness of the process. Some commonly used techniques include:
Stop words removal
Stop words are commonly used words that add little or no value to the analysis, such as "the," "and," or "but." Removing these words before analysis helps reduce the noise and vocabulary size to improve the accuracy of the results.
However, in contemporary approaches, particularly considering developments up to 2021, this practice may vary based on the specific context. While traditionally significant, its relevance and application may differ in modern methodologies. For a tongue-in-cheek take on the matter, you can read the article 10 Reasons Why You Shouldn’t Remove Stop Words to get another perspective on the subject.
Stemming and lemmatization
Stemming and lemmatization are text-processing techniques that reduce words to their root forms, disregarding tense or number variations. Stemming achieves this by removing suffixes from words, while lemmatization utilizes vocabulary and morphological analysis to convert words into their base form. For example, both "running" and "runs" would be reduced to "run."
However, it's essential to note that lemmatization and tokenization techniques have evolved significantly in recent years, departing from traditional Bag of Words approaches. Contemporary methods focus more on morphemes and finer linguistic units than simple word segmentation.
Moreover, modern tools and libraries often integrate lemmatization and tokenization functionalities alongside other NLP tasks. These tools provide ready-to-use tokenizers and lemmatizers that leverage advanced techniques, reflecting the advancements in NLP up to 2023 and beyond.
Named entity recognition (NER)
NER, short for Named Entity Recognition, utilizes statistical or predictive algorithms to identify and extract entities like names of individuals, organizations, locations, and dates from text. These algorithms are typically pre-trained on datasets where human annotators have labeled entities with predefined categories. Popular NLP processing tools and libraries like Spacy and NLTK offer built-in NER functionality.
Sentiment analysis
Sentiment analysis can analyze the text's opinions, attitudes, and emotions. Machine learning algorithms are trained to classify text as positive, negative, or neutral, providing insights into customer feedback and opinions.
Text classification
Text classification involves categorizing text into predefined classes or categories using techniques such as deep learning models, support vector machines (SVM), Naive Bayes classifiers, and convolutional neural networks (CNN). For instance, SVM and Naive Bayes classifiers in language detection are commonly employed to classify text into different languages based on linguistic features. In fraud and online abuse detection, deep learning models like CNNs are utilized to analyze patterns in text data and identify suspicious or abusive behavior. Additionally, urgency classification for customer support tickets in content management systems (CMS) often utilizes SVM or Naive Bayes classifiers to prioritize and route tickets efficiently based on their content. These techniques enable automated text classification, enhancing tasks like spam filtering, document organization, and customer support management.
Text clustering
As the name suggests, text clustering groups similar text documents together based on their attributes or features. It helps identify patterns and similarities in large text datasets for market segmentation, customer profiling, and recommendation systems.
Information extraction from unstructured data
NLP text mining techniques can also extract specific information or data from text, such as names, dates, numbers, and entities. Text extraction helps automate processes such as data entry, content extraction, and information retrieval.
Text summarization
Text summarization techniques use natural language processing and machine learning for text mining algorithms. These techniques help condense long, complex documents into shorter summaries while preserving the key information and meaning.
Text summarization generates executive summaries, abstracts, and bullet points to save time for readers and facilitate decision-making.
Text Mining Applications in Business
Applications of text mining techniques in business are endless. Some common applications include:
Understand customer sentiments: Analyze reviews, surveys, and feedback
Customer sentiments reflect their satisfaction levels and the likelihood of repurchasing. Reviews, surveys, and feedback data can be analyzed using NLP techniques for sentiment analysis.
With sentiment analysis, businesses can identify common issues or complaints and areas where customers are satisfied. This information can then be used to improve products and services and ultimately enhance customer satisfaction.
For example, a restaurant can analyze customer reviews to identify the most popular dishes and improve them further. Similarly, an e-commerce store can analyze feedback data to understand customers' preferences and offer better product recommendations.
Gauge market trends: Extract insights from social media and forums
Social media platforms and online forums are a goldmine of information for businesses. By analyzing the text data from these sources, businesses can gain insights into market trends, customer preferences, and competitors' strategies.
With text analytics and NLP, businesses can monitor conversations and discussions about their products or the industry. The data can be used to identify emerging trends, popular topics, and sentiments toward the brand or competitors. So, you can use text mining to make data-driven decisions for their marketing strategies and product development.
Assess brand perception: Monitor online mentions
As your business grows, so does your online presence and reputation. By monitoring online mentions, businesses can understand how customers, influencers, and the general public perceive their brand. The more a business knows about its brand's perception, the better it can manage its public image and make necessary changes.
Through word mining, businesses can track brand mentions on social media, review sites, and news articles. Text mining is used to identify potential brand advocates and influencers and negative publicity that needs to be addressed.
Improve communication: Automate email categorization
Emails are a primary means of communication for businesses. In fact, the average office worker receives 121 emails per day. With such a high volume of emails, manually sorting and categorizing them is time-consuming and prone to errors.
Businesses can automate email categorization using NLP techniques like topic modeling and named entity recognition. Email can be automatically organized into support, sales, or marketing categories. Promptly handling of emails and appropriate responses improve communication efficiency and customer satisfaction.
Detect fraudulent activities: Identify deception in text data
Businesses across industries are at risk of fraudulent activities, such as credit card fraud and insurance fraud. NLP techniques identify suspicious patterns in text data that may indicate fraudulent activities.
For example, insurance companies can use NLP to analyze claims and identify patterns of deception, such as excessive details or inconsistent information. Similarly, financial institutions can use NLP to flag suspicious transactions for further investigation.
Summarize documents: Generate concise overviews
Businesses generate vast text data daily, such as reports, articles, and contracts. Most of these documents contain important information buried in lengthy texts. With text summarization techniques, businesses can generate concise overviews of these documents and extract the most relevant information.
A document's key points and sentences can be identified automatically with NLP techniques like text classification. This is particularly useful for legal contracts, where businesses can quickly extract essential terms and conditions.
Offer personalized recommendations: Analyze user preferences
To keep customers engaged and satisfied, businesses need to offer personalized recommendations. With NLP techniques like collaborative and content-based filtering, businesses can analyze user preferences and recommend similar products or services. These recommendations can improve customer experience, increase sales, and foster brand loyalty.
How Text Mining and Text Analysis Software Can Be Customized to Your Business?
Businesses with unique needs or specialized industries can benefit greatly from customized text-mining software. If the available text-mining solutions do not meet your business requirements, consider customizing the software to fit your needs.
Industry-specific analysis
Each industry has its unique terminology, slang, and jargon used by professionals and customers. Generic text analytics tools may not accurately process and interpret this specialized language. However, by customizing the software, businesses can create industry-specific dictionaries and models to analyze text data more accurately.
For example, healthcare organizations can customize their text-mining software to identify and extract medical terminologies and diagnoses from patient reports. Similarly, legal firms can create customized dictionaries for legal terms and language used in contracts and court documents.
Customized feature extraction
Text data contains a wealth of information that can be valuable to businesses beyond simple sentiment analysis or topic modeling. By customizing text-mining software, businesses can identify and extract specific features from text data that are relevant to their needs.
As a text mining example, a restaurant wants to extract location information for reviews to determine which areas customers are most satisfied with. The location feature can be customized to identify city or neighborhood names from the text data and analyze sentiment accordingly.
Domain-specific dictionaries and taxonomies
Customized text-mining software can also be tailored to specific domains within an industry. It involves a complex process rooted in knowledge graphs and their ontological representations. While NLP is a fundamental component, it's just one piece of the puzzle.
In addition to industry-specific language, each business may have unique terminology and vocabulary. By customizing text-mining software, businesses can create domain-specific dictionaries and taxonomies to analyze their text data accurately.
For instance, there are multiple sub-domains in the legal sector, such as corporate law, family law, and intellectual property law. Each sub-domain has its specialized language, and customizing the text-mining software for each domain will yield more accurate results.
Tailored sentiment analysis
Sentiment analysis is a popular NLP application that identifies and extracts attitudes and emotions from text data. However, generic sentiment analysis may not accurately capture the sentiments of a particular industry or business. By customizing the software, businesses can train their models to identify and analyze sentiments specific to their domain.
For example, a hotel chain may want to analyze customer feedback for their new loyalty program. By customizing the sentiment analysis, the software can accurately identify and measure sentiments related to the loyalty program instead of overall customer satisfaction.
Integration with existing systems
The problem with many off-the-shelf text-mining software is its lack of compatibility with existing systems. For example, if a business wants to integrate text-mining software with its CRM or ERP systems, it may face difficulties if it is not customizable.
By customizing the software, businesses can ensure smooth integration with their existing systems and maximize the usefulness of their text data. Text mining can help improve various business operations, from customer service to supply chain management.
So, if generic text-mining solutions do not meet your business requirements, consider customizing the software to extract the most value from your text data.
Build Your Text Mining Software with iRonin.IT
Your business has unique needs, and off-the-shelf text-mining software may not meet all of them. At iRonin.IT, we offer custom software development services to build text-mining software for your business.
We can develop NLP applications to analyze text data, extract insights, and provide valuable recommendations. Our specialty verticals include finance, healthcare, e-commerce, and more.
With our machine learning and NLP technologies expertise, we can develop custom text-mining software that perfectly fits your business.
