Text Mining Definition
Text mining, also known as text data mining, is the process of extracting meaningful insights from written resources with the application of advanced analytical techniques and deep learning algorithms. This process includes a Knowledge Discovery in Databases process, information extraction, and data mining. Text mining also refers to the process of teaching computers how to understand human language.
What is Text Mining?
Text mining is the discovery process by which new information and patterns can be discovered and explored within unstructured data. This process typically involves the use of text mining tools, such as text mining in R and text mining Python; Natural Language Processing techniques; advanced analytics; and text mining algorithms to structure input text, identify patterns within the structured data, and interpret the results. Text mining tasks include concept extraction, document summarization, entity relation modeling, granular taxonomy production, sentiment analysis, text categorization, and text clustering.
Before text mining analytics can be applied, text data must first be transformed into a usable format. This is accomplished with text mining techniques such as tokenization, syntax parsing, and chunking. Once text has been cleaned up and pre-processed, text mining analytics techniques can then be applied:
- Information Retrieval: Information Retrieval (IR) software systems use algorithms based on a predefined set of queries and phrases to extract relevant data from tracked user behavior. Tokenization, which is the process of breaking up long-form text into smaller groups for use in cluster models, and stemming, which derives the root of a word by removing the prefix and suffix, are both types of IR.
- Natural language processing (NLP): Natural Language Processing text mining uses a variety of disciplines, including linguistics, artificial intelligence, computer science, and data science, to enable computers to “read” and understand both verbal and written human language.
- Summarization: creates a clear and concise summary of long-form text
- Part-of-Speech (PoS) Tagging: categorizes and tags each word’s part of speech (noun, verb, adjective, adverb, etc.) -- this eliminates ambiguity and facilitates semantic text analysis
- Text Categorization: a document is assigned algorithmically to a class or category based on predefined topics
- Sentiment Analysis: subjective information, typically involving customers’ perception of a. product or service, is extracted from internal or external data sources such as social media -- this. information is useful in gauging public opinion for marketing purposes
- Information Extraction (IE): relevant, structured information is automatically extracted from unstructured text documents and stored in a database - this process involves feature selection, which is the process of selecting the important subset of features and removing the rest, and feature extraction, which is the process of translating raw data into the inputs that a Machine Learning algorithm requires.
- Data Mining: the process of using methods such as Machine Learning, statistics, and database systems to identify new patterns in large structured and unstructured data sets -- text mining is a subset of data mining that focuses on textual materials.
Text Mining Software
Text mining software is available in a variety of both proprietary and open source formats. Capabilities may include: entity and theme extraction, topic categorization, sentiment analysis, document summarization, clustering, text analytics, terminology management, Natural Language Processing, geotagging, text data import, numeric conversion for Machine Learning, and text mining semantic analysis.
Text Mining Issues
The most challenging issue in text mining is the complexity and ambiguity of human language. The same word used in different contexts in the same document will have different meanings and therefore different interpretations. Ambiguity may be categorized as lexical ambiguity, syntactic ambiguity, semantic ambiguity, or pragmatic ambiguity. One technique for solving this issue, in addition to NLP, is the application of possibility theory, fuzzy set, and knowledge regarding the context to lexical semantics.
Other challenges and issues include:
- Cost: Semantic analysis methods are costly, slow, inefficient, and not scalable for very large text documents.
- Multilingual text refining: Text refining algorithms must produce language-independent intermediate forms and be capable of processing multilingual documents.
- Domain Knowledge Integration: Domain knowledge is not included in text mining tools and must be integrated manually, via the evaluation of coefficient signs in a logistic regression model and by analyzing a decision table extracted from a decision tree or rule-based classifier.
- Usability: Text mining tools are designed for trained knowledge specialists and are typically too complex for the average management executive or technical user.
Text Mining Examples
Text mining is applied throughout a wide variety of industries. Some text mining application examples include:
- customer service: businesses can improve the customer experience by gathering customer feedback with a variety of text analytics tools and feedback systems, and text mining and sentiment analysis can help isolate and prioritize customer issues, facilitating real-time responses.
- risk management: Text analytics for finance and business helps monitor shifts in sentiments, extracting information from analyst reports in order to derive insights from industry trends.
- maintenance: Decision making can be automated by detecting patterns related to problems with product/machinery functionality and the reactive and preventative maintenance processes.
- healthcare: Automated information clustering and extraction is crucial for medical research.
- spam filtering: Filtering methods may be applied to email text in order to block spam emails and minimize the threat of cyber-attacks on users.
What is Text Analytics?
Text mining and text analytics are largely synonymous. Both processes involve leveraging relevant information from unstructured, textual data; however, the difference between text analytics and text mining lies in the application. Text mining is essentially the process of cleaning up data so that it is available for text analytics.
Text analytics refers to the application of linguistic and statistical Machine Learning techniques to the information content of textual sources, specifically in the context of business intelligence and exploratory data analysis. By first transforming data into a more structured format with text mining analysis, more quantitative insights can be discovered in the process of analyzing texts.
Text Analytics Tools
Text analytics programs help users sort, analyze, and understand unstructured, text-based data, in which large quantities of valuable information is hidden. Text analytics software systems offer the following capabilities:
- A user-guided machine learning process (SAS)
- Custom, automatically generated topics and rules with visualizations to help guide any necessary adjustments and refining (SAS)
- Artificial intelligence tools, such as Language Understanding intelligent service, which helps bots better understand human language (Microsoft)
- User-friendly interfaces for teams with limited technological experience (Rocket Enterprise, Voyant Tools)
- Cognitive technology that is capable of assessing sentiment and emotions from text (Watson)
- Tagging capabilities that help organize and identify relationships between entities in unstructured data (Open Calais)
- Automatic defining of tags and categories using intelligent tags, which helps users quickly and easily sort through unstructured data archives to find specific information (Bismart)
Does OmniSci Offer a Text Mining Solution?
Businesses are increasingly turning to data science to help process, detect patterns, and gain insights from enormous volumes of unstructured data. Data scientists conduct data mining, along with other exploratory work, regression, predictive analysis, and qualitative analysis. This valuable information can be extracted and analyzed to help businesses increase efficiency, decrease costs, and improve the customer experience.
The OmniScidata science platform provides accelerated exploratory data science dashboards and modeling capabilities, interactive spatiotemporal visualizations, and deep behavioral analytics for easily tracking behavior patterns and customer sentiments, establishing relationships and identifying trends, quickly sorting and categorizing enormous textual datasets, and isolating and analyzing only the most relevant information.