Text Mining

Text mining, also called text data mining, is the process of analyzing unsorted text in order to transform it into a structured format. This enables easier identification of existing patterns and insights that would not be obvious in the unstructured data. Text mining is similar to the process called text analytics, and the two are often used interchangeably in conversations, with the difference being very nuanced — text mining provides qualitative insights, while text analytics grants quantitative results.

Text mining consists of several key steps in order to grant meaningful insight:

• Data collection: Placing all information in a readable form.
• Text parsing: Extraction of words, speech and examination of synonyms in order to simplify the text.
• Text filtering: Filtering out irrelevant terms, this can be automatic and/or based on custom filtering.
• Transformation: Counting the prevalence of terms, creating matrices for easier reading.
• Mining – Topic extraction, analysis that links parts together and predictive analytics (among other things).

The final three steps can be repeated with feedback taken into account, refining the process in order to give the most relevant analysis for your particular aims.

Common applications of text analytics

Text mining is a useful tool, with broad applications in customer service, risk management and maintenance. It’s increasing speed and accuracy has allowed real-time adjustment of online tools such as chatbots and personalized web pages, allowing for customer experience to be improved during the process and not simply after the fact with the benefit of hindsight. Text analytics has also been used with great effect in market risk analysis, allowing investors to predict trends and shifts in financial markets by extracting information from reports and whitepapers — something impossible for a human to perform with such speed.

The methodology of text-mining tools

Text mining tools are designed to extract information using techniques such as language analysis and text tagging. Below you’ll find a few classes of these methods:

• Information Retrieval: The process of enabling relevant information to be identified, by breaking longer texts into sentences/words called tokens, reducing the complexity by tagging synonyms and removing prefixes/suffixes in order to derive meaning.
• Natural Language Processing (NLP): Analyzing grammar, shortening longer pieces of text into concise summaries of the document’s main points, analysing tone with sentiment analysis and categorizing into different topics.
• Information Extraction: Selecting the important features of the text, identifying named entities such as locations or names, and selecting the important features that are to be focused on in the analysis.