Introduction to Text Mining and Text Analytics
– Text mining is the process of deriving high-quality information from text.
– It involves automatically extracting information from written resources such as websites, books, emails, reviews, and articles.
– High-quality information is obtained by devising patterns and trends using statistical pattern learning.
– Text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis, and document summarization.
– Text mining involves the application of natural language processing, algorithms, and analytical methods to turn text into data for analysis.
– Text analytics uses linguistic, statistical, and machine learning techniques to model and structure the information content of textual sources.
– It is synonymous with text mining and is used in business settings.
– Text analytics is used to respond to business problems and extract knowledge from unstructured text.
– 80% of business-relevant information originates in unstructured form, primarily text.
– Text analytics discovers and presents facts, business rules, and relationships that are otherwise locked in textual form.
Applications of Text Mining
– Text mining technology is applied to government, research, and business needs.
– Legal professionals use text mining for e-discovery.
– Governments and military groups use text mining for national security and intelligence purposes.
– Scientific researchers use text mining to organize large sets of text data and support scientific discovery.
– In business, text mining is used for competitive intelligence and automated ad placement.
– Text mining software packages are marketed for security applications.
– They are used for monitoring and analyzing online plain text sources for national security purposes.
– Text mining is also involved in the study of text encryption/decryption.
– Text mining has various applications in the biomedical literature.
– It assists with studies in protein docking, protein interactions, and protein-disease associations.
– Text mining facilitates clinical studies and precision medicine with large patient textual datasets.
– It helps in the stratification and indexing of specific clinical events in electronic health records.
– Text mining algorithms analyze symptoms, side effects, and comorbidities in healthcare data.
– Text mining methods and software developed by major firms like IBM and Microsoft.
– Efforts in the public sector to create software for tracking and monitoring terrorist activities.
– Weka software as a popular option for study purposes.
– NLTK toolkit for Python programmers.
– Gensim library for advanced programmers focusing on word embedding-based text representations.
Online Media Applications
– Text mining used by large media companies like the Tribune Company to clarify information and improve search experiences.
– Editors benefit from text mining by being able to share, associate, and package news across properties.
– Increased opportunities to monetize content through text mining.
– Improved site stickiness and revenue through better search experiences.
– Use of text mining on the back end of online media platforms.
Business and Marketing Applications
– Text analytics used in business, particularly in marketing and customer relationship management.
– Application of text mining to improve predictive analytics models for customer churn.
– Text mining applied in stock returns prediction.
– Use of text mining to gain insights into customer behavior and preferences.
– Integration of text mining in marketing strategies for targeted advertising.
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).
Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods. An important phase of this process is the interpretation of the gathered information.
A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The document is the basic element when starting with text mining. Here, we define a document as a unit of textual data, which normally exists in many types of collections.
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