Data mining is a phrase that is found throughout the lexicon of modern digital terminology that refers to the collection of information with the express purpose of making categorical definitions as to its meaning and relevance. However, the process becomes very complex at lower levels where the separations begin to have some greater significance to the entity retrieving the raw data. One of the methods used to reveal this is called text mining, which takes the character sets and breaks them down into a usable format that can be analyzed by a program or algorithm. At these later stages, the most common groups of data begin to emerge as more is added to them. These complicated analyses and categorizations are then used to do things like extrapolate assertions, predict behaviors, understand reactions and too many others to list here.
Once extracted, text is further reduced to its key components by one or both of two approaches: linguistic and nonlinguistic. These two techniques are quite different in strategy, and efficacy, as well as the nature of the results they return. Nonlinguistic technology functions on statistics-based rules that count word frequency and statistical odds of connection to any given concept. To counter the expected inaccuracy of this method, additional rules are applied to create a complicated system of determining relevance known as rule-based. Linguistic tactics use natural language processing (NLP) to interpret the meaning of text, by understanding things such as syntax, phrase structure, context, and the underlying language, to group extractions into concept clusters.
The Linguistic angle functions in much the same way that a person would to understand meaning in a conversation by recognizing patterns of speech like context and sentence structure. This allows the listener, or reader, to make judgments on the intent of a certain statement or question and eliminate confusion. This is very similar to the way NLP operates by comprehending language such that incorrect associations are limited and less ambiguous outcomes establish this as a more reliable approach. Once the information is separated into collections, it can be further dissected into arrangements that explain what the user is looking for more readily. The nature of what the search is targeting will be the deciding factor that ultimately reveals the final dataset.
Data mining is a very popular tool that has a host of refining implements that can be used in conjunction with it to hone data into nearly any deviation being sought. Two examples used to achieve a deeper recognition of raw text contents and their actual purposes are collocation and sentiment analysis. Collocation examines documents and pages for recurrences of the same word or phrase, which are unlikely to be random, within parameters set by the searcher. Finally, sentiment analysis, also known as opinion mining or emotion AI, distinguishes the difference between what is positive, negative, and neutral by using various emotion cues within the syntax. With all these apps and so many more at the ready, the Internet is a fount of endless elucidation.
