As fast as the data is increasing at exponential rates, all types of institutions, organizations and industries are accumulating their data electronically since the huge volume of data is streaming over the internet in the form of digital libraries, repositories, and other sort of textual information sources like blogs, social media networks, and e-mails.
Consequently, it has become challenging to dictate and discover required patterns and trends for drawing out worthwhile knowledge from such a large amount of data.
On the other hand, conventional data mining tools are incapable of managing unstructured textual data as it demands investing time and efforts to bring out information.
In rescue of it, the approach of text mining applications, tools, and techniques come in action in order to delve into unstructured data for deriving imperative patterns and insights. They are required for analyzing textual data sources.
Through this tutorial, we will discuss “text mining” and its processing, Methods and applications. At the end, a difference between data mining and text mining is also explained.
Text mining can be explained as the process of extracting essential information from standard language text data. This is the data that we generate via text messages, documents, emails, files that are written in common language text. Text mining is primarily used in context of extricating beneficial insights or patterns from such data.
In addition to that, text mining is the multidisciplinary field incorporating the tools of information retrieval, data mining, machine learning, statistics and computational linguistics. This deals with natural language text which is stored in semi-structured or unstructured formats.
In its simplest form, text mining explores facts, associations, and affirmation from the mass of unstructured textual data. This extracted information is then converted into structured format that can be further analyzed or showcased directly using HTML tables, mind maps, charts, etc. For this, text mining employs a wide range of methodologies to process the text.
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A large number of documents that contain unstructured and semi-structured data, text preprocessing is applied on it and transforms a raw text file into clearly-explained sequence of linguistically-meaningful units. Text preprocessing incorporates various kind of processing as following
Text Cleanup: It processes various tasks such as from removing advertisements from web pages to cutting out tables and figures, etc.
Tokenization: It makes segmentation of sentences into words by erasing spaces, commas etc.
Filtering: It extricates the words that have no relevant content-information including articles, conjunctions, prepositions, etc. Even the words of frequent repetitions are also removed.
Stemming: It is the process of transforming words to its stem,or normalized form by making basic forms of words to recognize words by its root word-forms. For example, the word “go” is the stem goes, going and gone.
Lemmatization: It reorganizes the word to correct root linguistically, that is the base form of the verb. During the entire process, the first step is to understand the context, and finds out the POS of a word in a sentence and at last identifies the ‘lemma’. For example, go is the lemma of goes, gone, going, went.
Linguistic processing: Involving Part-of-speech tagging (POS), Word Sense Disambiguation (WSD) and Semantic structure, it works as follow as
Part-of-speech tagging: to determine the linguistic category of the word by assigning word class to each token. It has eight classes: noun, pronoun, adjective, verb, adverb, preposition, conjunction and interjection.
Word Sense Disambiguous (WSD): to determine that a given word is ambiguous in a text, e.g., resolving the ambiguity in words “bank” and “financial institutions”. Basically, it assigns the most suitable meaning automatically to a polysemous word in a given context.
Semantic structure: Full parsing and partial parsing are known two methods for making semantic structures.
Full Parsing: makes a full parse tree for a sentence, and sometimes fails due to poor tokenizing, error in POS tagging, latest word, incorrect sentence breaking, grammatical inaccuracy, and many more.
Partial Parsing: Also known as word chunking, it makes syntactic constructs such as Noun Phrases and Verb Groups.
After the process of feature selection, text transformation conducts features generation. Feature generation reflects documents by words they contain and words occurrences where the order of word is not significant. It deploys bag-of-words or vector space models.
Here feature selection is the process of choosing the subset of significant features that are used in creating a model. It diminishes the dimensionality through excluding redundant and unnecessary features.
Various text mining techniques and methods such as classification, clustering, summarization, and many more are used. We will discuss about these technologies in next blog. (Source)
There are various techniques being developed to solve the text mining problems, they are basically the relevant information retrieval according to the requirement of users. Counting on the information retrieval techniques, some common methods are following;
Text Mining Methods
Term is defined as the word that has a well-explained meaning in a document. Under term based method, the document is inspected on the basis of terms and takes the benefit of productive computational performance while capturing the theories for term weighting.
Over the time, after the association of information retrieval and machine learning, the term based techniques have developed. It has some disadvantages as well,such as
Polysemy: means a word having multiple meanings, and
Synonymy: means multiple words have the same meaning.
This makes confusion for the users to understand the importance/meaning of a document. Information retrieval technique gives many of the term-based methods to solve such ambiguity.
Phrases provide more types of semantic information and are ambiguous. In this method, documents are anticipated on the basis of phrases as they are less doubtful and extra handy than individual terms. Some causes that impede the performance are
Because of secondary analytical properties to terms
Extensive replication and noisy phrases
Under this method, terms (words) are concluded on the sentence and document level, such text mining techniques are based on the analytical analysis of words and phrases. Here, analytical analysis considers the word significance without any document.
It often occurs that two terms might hold the same frequency in the same document, but one term contributes more meaning/significance than the other. Therefore, a new concept based text mining is introduced in order to accomplish the semantics of texts.
Content based mining method/model contains three components;
First component: It figures out semantic arrangement of sentences.
Second component: It decides a conceptual ontological graph (COG) that explains semantic structure.
Third component: It extracts major concepts on the basis of first-two components in an attempt to construct feature vectors via implementing standard vector space model.
Holding the ability to distinguish unnecessary terms and meaningful terms, this model explains a meaningful sentence and occasionally relies on NLP methods.
The Pattern based model performs better than any other pure data mining-based method.
Under this method, documents are examined on the basis of patterns where patterns are built in a taxonomy by applying a relation. Patterns can be identified by employing data mining techniques including association rule, frequent itemset mining, sequential and closed pattern mining.
In text mining, where this identified knowledge is important, it is also inefficient as there are some useful long patterns, with high selectivity, that need support. Even, most of the short patterns are useful (known as misconstrued patterns) and lead to ineffective performance.
As a consequence of it, an effective pattern discovery process is required to conquer low-frequency and miscontruction text mining problems. The pattern detected method employs two procedures “pattern deploying” and “pattern evolving” and refines the discovered patterns. (From)
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Text analytics has impacted many industries' work such that it aids in improving the user experience and makes agile and valued business decisions. Some of applications involved;
With advanced technologies,various ways are there through which a customer can give feedback via various means such as chatbots, customer surveys, online reviews, support tickets, and social media profiles. Combining feedback with text analytics tools can yield in improving customer satisfaction and experience with high speed.
Many companies use the process of text mining and sentiment analysis to prioritize key concerns for their customers and enable businesses to answer issues in real-time and enhance customer satisfaction.
In risk management, text mining can give information about industry trends and financial markets by regulating sentiment data and by obtaining information from analyst reports and whitepapers.
This approach is applicable in banking institutes where banking data can provide plenty of information while dealing with business investment across several sectors.
Text mining not only gives an adequate and complete picture of the operation but also provides the functionality of products and services.
It can automate decision making processes through identifying patterns that correlate with problems, preventive and reactive maintenance procedures.
It also helps maintenance experts in uncovering the major causes of challenges and failures.
Where the text mining techniques are extensively used in biomedical applications (specially in clustering information), these techniques are becoming increasingly valuable in the healthcare sector.
For example, in medical research, manual investigation demands a high cost and can be time-consuming, where text mining provides an automated solution for drawing out effective information from medical literature.
In the action to stop hackers from contaminating computer systems with malicious activities, or to infect with malware, spam filtering serves as an entry spot to prevent these activities.
Text mining gives a method to filter and expel such emails from inboxes, augmenting user experience and reduces the risks of cyber attacks.
In the simplest form, data mining is the process of exploring patterns and extracting information from large sets of data, and is practised to deduce raw data into meaningful information.
In contrast to it, text mining is fundamentally an AI technology involving the processing of data from various sources of text documents. Even, various deep learning algorithms are deployed for the adequate evaluation of text data. Also, text mining is a part of data mining.
The following table describes the fundamental differences between data mining and text mining;
Contains functions for searching patterns and association in structured data
Involves functions for making unstructured textual data into structured format to conduct data analysis
Structured data found from systems like
databases, spreadsheets, ERP, CRM and accounting applications
Unstructured textual data found from in emails, documents, presentations, videos, shared files, social media and the internet.
Structured data is homogenous and well-organized that makes it convenient to retrieve
Unstructured textual data stores in different formats (heterogeneous), text is located in a diverse range of applications and systems, and thus difficult to retrieve.
Structured data is in formal format and simplifies the process of consuming data for analytical models.
Linguistic and statistical analysis techniques ( NLP keywords and meta tagging) are used to make unstructured data into informative structured data.
Need for taxonomy (Classification)
There is no need to construct a crucial taxonomy for text mining.
As the unstructured text is in different types and formats, a specific taxonomy is required to make data to be organized into a general framework.
As the technology advances & changes rapidly, data volumes are expanding which is mostly unstructured. This new flood of big data demands for most enterprises to adjoin both structured and unstructured data in order to deliver huge visibility and extended insights into their business and operations.
Today, incorporating both data and text mining yielding in most efficient and true data-driven decision-making.
Being an interdisciplinary field from computational linguistics & NLP, information extraction, information retrieval, machine learning and data mining, Text mining is the core process of deriving non-trivial information from the unstructured textual data.
It extracts trends and features from unstructured text data after applying text mining techniques to discover knowledge. Thereby, this is a very essential process to extract hidden meaningful information and knowledge from textual data.
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