Text mining algorithms are nothing more but specific data mining algorithms in the domain of natural language text. The text can be any type of content – postings on social media, email, business word documents, web content, articles, news, blog posts, and other types of unstructured data.
Algorithms for text analytics incorporate a variety of techniques such as text classification, categorization, and clustering. All of them aim to uncover hidden relationships, trends, and patterns which are a solid base for business decision-making.
On this page:
- A list of the top text meaning algorithms.
- Infographic in PDF.
1. K-Means Clustering
K-means clustering is a popular data analysis algorithm that aims to find groups in given data set. The number of groups is represented by a variable called K.
It is one of the simplest unsupervised learning algorithms that solve clustering problems. The key idea is to define k centroids which are used to label new data.
K-Means Clustering is a classical way for text categorization. It is widely used for document classifications, building clusters on Social Media text data, clustering search keywords and etc.
Using k-means clustering for text data requires doing some text-to-numeric transformation of our content data. If you work with R, you might know that it has various packages to simplify the process.
2. Naive Bayes Classifier
Naive Bayes is considered one of the most effective data mining algorithms. It is a simple probabilistic algorithm for the classification tasks.
The Naive Bayes Classifier is based on the so-called Bayesian theorem and gives great and reliable results when it is used for text data analytics.
Naive Bayes classifier is not a single algorithm but a family of algorithms which assume that values of the features used in the classification are independent.
It is very easy to code with the standard programming languages such as PHP, JAVA, C#, etc.
As one of the best text classification techniques, Naive Bayes has a variety of applications in email spam detection, document categorization, email sorting, age/gender identification, language detection and sentiment analysis.
3. K-Nearest Neighbor (KNN)
K-Nearest Neighbor (KNN) is also one of the most used text mining algorithms because of its simplicity and efficiency.
KNN is a non-parametric method that we use for classification.
In a few words, KNN is a simple algorithm that stores all existing data objects and classifies the new data objects based on a similarity measure.
In the text analysis domain, it is used to check the similarity between documents and k training data. The aim is to determine the category of the test documents.
One of the biggest text mining applications of KNN is in “Concept Search” (i.e. searching for semantically similar documents) – a feature in software tools, which is used for helping businesses find their emails, business correspondence, reports, contacts, etc.
4. Support Vector Machines (SVM)
This approach is one of the most accurate classification text mining algorithms.
Practically, SVM is a supervised machine learning algorithm mainly used for classification problems and outliers detections. It can be also used for regression challenges.
SVM is used to sort two data sets by similar classification. This data analysis algorithm draw lines ( known as hyperplanes) that separate the groups according to some patterns.
The goal of SVM is to create this hyperplane. The hyperplane with the maximum margin from both groups is best. In the real world, SVM can model complex problems such as text and image classification, hand-writing recognition, face detection, and biosequence analysis.
When it comes to text mining, SVM is widely used for text classification activities such as detecting spam, sentiment analysis, document classification into categories as news, emails, articles, web pages, etc.
5. Decision Tree
Decision Tree algorithm is a well-known machine learning technique for data mining that creates classification or regression models in the shape of a tree structure.
The structure includes a root node, branches, and leaf nodes. Each internal node indicates a test on an attribute and each branch indicates the result of a test. Finally, each leaf node indicates a class label.
Decision Tree algorithm is nonlinear and simple.
As a text mining algorithms, Decision Trees has many applications such as analyzing all the text that comes from customer relationship management. It is also used in making medical predictions based on medical history documents and etc.
6. Generalized Linear Models (GLM)
Generalized Linear Models is a popular statistical technique used for linear modeling.
Actually, GLMs combine a large number of models including linear regression models, logistic regression, Poisson regression, ANOVA, log-linear models and etc.
Combining the linear approach with data mining tools has many advantages such as accelerating the modeling process and achieving better accuracy.
7. Neural Networks
Neural networks are nonlinear models which represent a metaphor for the functioning of the human brain.
Despite that Neural networks have a complex structure and long training time, they have their place in data analysis and text mining algorithms.
In the domain of text analytics, Neural network can be used for grouping similar patterns, for classifying patterns, and etc.
The application of the neural network is important in data mining because of some characteristics such as self-organizing adaptiveness, parallel performance, fault tolerance, and robustness.
When it comes to text data analysis, neural networks are popular in the area of medical research documents, finance, and marketing content mining.
8. Association Rules
Association rules are just if/then statements that aim to uncover some relationships between unrelated data in a given database.
They can find relationships between the items which are regularly used together.
Popular applications of association rules are basket data analysis, cross-marketing, clustering, classification, catalog design, etc. For example, if the customer buys eggs then he may also buy milk.
Using this approach in the are of text data mining, can help users to gain knowledge from the collection of the different type of content such as web documents (to decrease the time for reading all those documents).
Another example is, the association rules used for identifying positive or negative associations between symptoms, medications, and laboratory results and medical text data reports.
9. Genetic Algorithms
Genetic algorithms or evolutionary algorithms are a family of stochastic search algorithms witch mechanism is inspired by the process of neo-Darwinian evolution.
Naturally, GAs have applied binary strings (chromosomes) to encode the features that form an individual. They basically try to imitate the human evolution.
The reason for using GAs for data mining is that they are adaptive and robust search techniques.
GAs can solve several text data mining problems such as clustering, the discovery of classification rules, attribute selection and construction.
10. Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation is one of the techniques which currently is used in topic text modeling.
In fact, latent Dirichlet Allocation (LDA) is a generative probabilistic model designed for collections of discrete data (to know what is discrete data see our discrete vs continuous data post).
To put in another way, LDA is a method that automatically finds topics that given documents contain.
LDA has several advance versions (dynamic, correlated, and etc.) which have a variety of applications in an information retrieval.
For example, if you have a ton of documents (such as emails) and you want to find out what they are about without the need to read them. In this case, LDA can give you several topics which are characterized by the most probable words.
Want more text mining algorithms?
Here is a list of other popular algorithms used in the area of text analytics:
- Apriori (which is a famous association rule algorithm)
- Non-Negative Matrix Factorization
- Minimum Descriptor Length
- Sequential algorithms
- Divisive algorithms
- Fuzzy clustering algorithms
- Hierarchical algorithms
- Agglomerative algorithms