What Is Text Mining?

Text mining is a technique for processing and exploring extensive text data. Organizations have substantial knowledge bases of text data from documents, emails, social media, support tickets, and more. Text mining uses AI technologies that can understand and work with text data similar to humans. Text mining tools filter, sort, and classify text to uncover hidden patterns and relationships. Organizations can transform raw text data into practical knowledge and new insights for business intelligence.

Text mining leverages artificial intelligence to deliver several benefits to businesses.

Discover insights

Text mining tools can process text documents at speeds that far exceed human performance. They can scan large volumes of data, such as documents or customer support tickets, to reveal trends and identify patterns. Businesses can turn the observations into actionable insights to improve their operations.

Simplify unstructured data

Several sectors must contend with lengthy documents, like legal and insurance files, that are complicated to examine. Organizations use text mining to streamline document processing and rapidly extract key phrases, topics, sentiments, and other essential information from the documents. Text mining tools can filter, sort, and summarize documents with minimal human involvement and high precision.

Adaptable to business needs

Organizations can train text mining solutions with specific terms related to their company operations. By learning these new terms and phrases, businesses can use text mining to classify and tag documents with terms that relate specifically to their company.

Information redaction

Text mining allows organizations to scan through documents and redact specific phrases or sensitive information. After identifying what you want to redact, you can use text mining techniques to protect your sensitive data and hide personally identifiable information (PII) from all documents.

Text mining uses natural language processing (NLP) and other artificial intelligence technologies to deliver several use cases to businesses.

Call center analytics

After cataloging call center transcripts, businesses can use text mining algorithms to detect customer sentiment. By scanning through transcriptions, the tools reveal how customers feel during their interactions with customer support. Customer service teams can monitor the success of their employees or develop training data that shows a perfect customer call for employee onboarding purposes.

Quantify product reviews

Businesses can collect social media posts, product reviews, and other relevant information for textual analysis. By using text mining, they can determine how positive or negative the collected customer feedback is. From there, they can use this information to identify common threads in positive and negative reviews. For example, many negative reviews include comments about a product's poor quality of materials. By pinpointing this information with text mining, businesses know what to change to improve customer satisfaction.

Manage legal briefs

With text mining, organizations can streamline examining and extracting insights from legal documents. They can find specific information in larger court documents and contracts, identify PPI in documents, and redact sensitive information. This technique is especially useful when circulating parts of records to the public while protecting personal data.

Process financial documents

Text mining analyzes texts to find specific relationships and connections. By using text mining on financial documents, businesses can identify patterns in financial documents and articles. They can also extract information from financial records, like what a provider will cover when a company makes an insurance claim.

Text mining uses natural language processing, machine learning, and rule-based systems to identify patterns, trends, and relationships in text documents. After collecting unstructured data a business wants to analyze, text mining tools do the following.

Preprocessing

Text mining technologies preprocess documents as a first step in text mining. Tasks they perform include:

• Decomposing text into words, phrases, symbols, or other meaningful elements called tokens.

• Standardizing or normalizing text by converting it to a consistent format (e.g., lowercase all letters).

• Eliminating common words (e.g., and, the) that add little value to the search.

• Reducing words to their base or root form (lemmatization) to improve the match between different word forms.

Similar to document processing, any query issued by the user for information retrieval undergoes preprocessing (tokenization, normalization, etc.) before it is used to search the data.

Natural language processing

Natural language processing (NLP) technology is the core of text mining. Once preprocessing is complete, NLP algorithms process words, building up the context of what the document is saying. They segment and analyze the relationships between different parts of the text.

NLP techniques use AI algorithms to analyze documents' grammar, punctuation, and syntax and understand them like humans. They perform syntactic analysis on a sentence level and semantic analysis on a word level to understand human language.

Information search

Searching is a crucial feature in text mining. The text mining solution indexes the documents to facilitate fast and accurate retrieval. Indexing involves creating an inverted index, which is a mapping from content terms to their locations in the documents. This index is critical for efficient querying.

The system uses algorithms to search the indexed data based on the processed query. This can involve:

• Using AND, OR, and NOT operators to find documents that match boolean criteria.

• Representing documents and queries as vectors in a multidimensional space and finding documents similar to the query based on the cosine similarity measure.

• Estimating the probability that a document is relevant to a query based on specific criteria and returning the most probable results.

The retrieved documents are ranked based on their relevance to the query. Relevance scoring can consider factors like term frequency (TF) and inverse document frequency (IDF).

Post-processing

Post-processing tasks vary depending on the goal of the specific text mining activity. For example, you can:

• Summarize unstructured data documents into concise texts.

• Tag speech with word types to allow semantic analysis.

• Categorize text into subtopics and analyze the sentiment of writing.

• Extract specific document sections for separate storage and analysis.

The final steps vary accordingly. For example, category labels may be added to the top of the file, documents may be annotated, or data may be stored in a database.

Text mining and text analysis are closely related fields that use AI and ML technologies to interact with unstructured text data. However, they are not the same and have different purposes, scope, and methods. Text mining produces qualitative data, while text analytics converts this information into quantitative data.

Purpose

Text mining techniques mainly focus on extracting knowledge or specific information from a document. It aims to find hidden patterns, connections, and relationships in the data.

Text analytics further analyzes the relationships that text mining discovers. This process aims to interpret the data to find out why these may occur and what we can learn from them.

Scope

Text mining works with unstructured data and has no limit on scale or size. Text analytics has a more limited scope, as it uses the information that text mining produces. It takes these insights one step further, using text analytics tools to analyze connections and relationships in the text.

Text mining works on unstructured and semi-structured data, turning it into structured data that text analytics uses.

Methods

Text mining uses data collection and storage, data preparation, and natural language techniques to deliver insights. Text analytics only uses natural language processing, as text mining performs pre-processing tasks.

While text data mining is a powerful technology, it does have a few challenges.

Data collection

Text mining requires large information sets to find a statistically significant trend. Storing these data volumes can pose a challenge for organizations that don't have the data architecture in place to support these requirements.

Data quality

Large volumes of unstructured data may have inconsistent quality. If documents contain spelling mistakes, general errors, or irrelevant information, text mining tools may be unable to understand and extract insight.

Potential ambiguity

NLP models require considerable investment and training to function well. After acquiring large amounts of data, businesses still need to train text mining algorithms on their internal documents to understand the terms they use in their organization. Due to language ambiguity, these models must undergo rigorous testing before delivering reliable insights.

Amazon Web Services offers robust tools that allow businesses to extract valuable information from documents. Businesses can store large volumes of textual data with Amazon S3 before then using Amazon Comprehend to extract key phrases, sentiments, and entities from textual data. With Amazon Comprehend, businesses can:

• Simplify the process of extracting important information from documents and identify key phrases, topics, sentiments, and more.

• Redact information and PPI from business documents to protect your company's sensitive information.

• Discover valuable connections and uncover relationships in text documents.

• Personalize Comprehend's training model to understand terms unique to your business or sector without needing any machine learning experience.

Get started with text mining on AWS by creating a free AWS account today.