With the increasing popularity of textual data over the Internet, it has become imperative to provide a definitive outline to this unstructured data, and extract information to upgrade user experience. The storage, processing, and analysis of data, is now applied to text sources like blogs, web pages, and other digital literature to detect patterns, trends, and debates. Such procedures are collectively grouped under the term text mining and text classification.
For those who wish to pursue a career in data science, they can upskill themselves by learning more about text mining and text classification techniques.
What is Text Mining?
A type of data mining, text mining involves the use of various methods and techniques to retrieve previously concealed information from unstructured textual data and find patterns that would contribute to decision-making. Several techniques from different applications of computer science, such as information retrieval, statistics and machine learning are used to process the data.
Text Mining Techniques
Information Extraction
Also known as IE, it is the most reliable form of text mining technique that involves extracting information from massive chunks of unfiltered and unsorted data that is in textual form. Let us say a text is largely unstructured and one needs to find out its meaning.
It can be deciphered via information extraction techniques whereby keywords, main features, certain entities and their interlinks are identified within the text and the newly-gained information is stored within an associated database for processing and reference in a later timeline.
Information Retrieval
Although information retrieval might sound similar to Information Extraction, the difference lies in the process of extraction of information. Unlike information extraction, where textual data is collected first, and then analysed for detecting patterns, in information retrieval, we already have a given textual parameter, such as a given set of keywords or key phrases to be detected.
For this purpose, text miners harness diverse kinds of algorithms for monitoring the behavioural pattern of consumers and gathering relevant data accordingly. Query or question-based algorithms used in popular search engines for catering to trends and for collecting relevant information based on web searches on the internet are major applications of information retrieval.
Categorisation
Under this text mining technique, free format or independent texts are allocated to predefined classes or a set of topics depending upon the inputs and outputs generated by the content. This process makes use of approaches such as dimensionality reduction and pre-processing so as to instruct classifiers in sorting the text into user-defined categories with the help of some familiar examples.
Once trained in this manner, classifiers can categorise unrecognised examples with much ease. Naïve Bayes Algorithm and Support Vector Machine Algorithm are some of the functional models used for categorisation in machine learning.
Clustering
As the name indicates, clustering sorts text documents into certain groups or “clusters” by identifying the basic structure of the arrangement of textual information. In this method, the algorithm extracts some similar patterns from the given textual data, organises them either downwards from the top or from the bottom to upwards.
Consequently, in one cluster, the assorted documents will evince an extremely high percentage of similarity and there will be high contrast among the clusters. In other words, the quality of the outcome generated by a clustering algorithm will be determined by the number of clusters generated with high intra-cluster similarity and significantly low inter-cluster similarity.
Summarisation
The primary aim of text summarisation is to reduce the length and simplify the complex details of a textual document without compromising on vital information. Using summarisation techniques for text mining, one can determine whether a text document is worth perusal for a reader.
The algorithm searches through numerous text sources, prepares their summaries and keeps the original meaning the same.
Text Classification
Text classification is a subset of text mining and it involves allocating a number of predefined labels to a certain textual information. It involves analysis of the topic, detecting the language of the text, identifying the tone and intention to filter spam or toxic data or to understand customer reviews- all to obtain critical insights about textual data. We have provided some of the most popular text classification techniques which perform the aforementioned tasks.
Types of Text Classification Techniques
Sparse Vectorisation
This is a standard approach to text classification whereby a text document gets converted into several vectors and then it is classified using machine learning algorithms like logistic regression. The process of converting a text passage into a vector can be achieved by using the term frequency-inverse document frequency.
For a set vocabulary, this algorithm will generate a one-dimensional vector corresponding to each word in the vocabulary. Every part of the vector will reflect how many times the corresponding word appears in the input textual information when compared to a group of textual passages.
Dense Vectorisation
A major flaw in the sparse vectorisation technique of text classification is that it disregards the sequence of the words in the texts, as well as the factor that many words bear semantic resemblance to each other. Furthermore, if a word is polysemous in structure, that is, it offers multiple interpretations based on context, then sparse vectorisation fails to derive the contextual differences.
In such a situation, dense vectorisation technique can be quite helpful as it helps address the aforementioned problems by mapping embedded sentences to real vector numbers with the help of a pre-trained algorithm for language representations.
Natural Language Inference
When the classes were previously unknown and there is no prior instance of training present, then natural language inference models can be employed for text classification. A text classification model which has been attuned to natural language inference will choose a text passage as the premise, and try to verify whether the premise leads to, nullifies, or provides a neutral relationship with the hypothesis defined by the class.
Conclusion
Text mining and Text classification techniques have become highly sought-after methods in various industries that rely on consumer data for providing service. From marketing to sales, financial services to healthcare, mining and classifying textual data sources using machine learning models can save a lot of time, and prevent losses.
To enhance their skills in designing data mining models and algorithms, professionals can enrol in a data analytics course or a data science course such as the Imarticus Learning’s Postgraduate Program in Data Science and Analytics and take a major step towards cementing their career in data science.
