What is text mining? Applications and preprocessing techniques
Text mining, also called text data mining, is the process of deriving high-quality information from written natural language. High-quality information refers to information that is new, relevant and of interest for the project at hand. All of the data that we generate via emails, documents, PDF files and text messages are written in natural language, but this data isn’t typically stored in a structured format. Text mining is the process that we use to draw insights and patterns from that unstructured data.
For example, scanning a set of documents written in natural language is a simple text mining task. After scanning, you would either model the documents for predictive classification purposes, or populate a clean database with the extracted information.
What is the difference between text mining and text analytics?
Text mining is roughly synonymous with text analytics, and many people use the two terms interchangeably. But by strict definition, text mining is a step prior to text analytics in the grand process of your machine learning projects.
Text mining is the process of cleansing data. The overarching goal of text mining is to convert text data into a standard format, using natural language processing and analytical methods for information retrieval. You should end up with a clean, organized dataset, most likely in an Excel or csv file.
Once your data has gone through text mining, it’s ready for text analytics, which is the process of applying statistical and machine learning algorithms. The goal of text analytics is to detect patterns in the data, and use it to predict or infer new insights.
What data preprocessing techniques are used in text mining?
A few of the most common preprocessing techniques used in text mining are tokenization, term frequency, stemming and lemmatization.
Tokenization: Tokenization is the process of breaking up text into separate tokens, which can be individual words, phrases, or whole sentences. In some cases, punctuation and special characters (symbols like %, &, $) are discarded in the process.
Term frequency: Term frequency tells you how much a term occurs in a document. Terms can be either individual words or phrases containing multiple words. Since documents differ in length, it’s possible that a term would appear more times in longer documents than shorter ones. Thus, you can calculate term frequency by dividing the number of times the term appears, by the total number of terms in the document, as a way of normalization. Term Frequency = [Number of times the term appears in the document] / [Total number of terms in the document]
Stemming: Stemming is the process of reducing words to their root form. For example, we would reduce the word robotics to the stem robot. The stem is usually a full word, but does not need to be. For example, the Porter stemmer, a widely used algorithm for removing common suffixes from English words, reduces the words universal, university, and universe to the stem univers.
Lemmatization: As we saw with the Porter stemmer example, the simple suffix rules that are commonly used for stemming could modify the stem. Lemmatization is a more complex approach to determining word stems, which addresses this potential problem. In lemmatization, we use different normalization rules depending on a word’s lexical category (part of speech). This way, the stemmer can grasp more information about the word being stemmed, and use that to group similar words more accurately.
Text mining methods
A few of the most common text mining techniques include information extraction, information retrieval, categorization, clustering and summarization.
Information retrieval: This method refers to the process of returning information that is relevant to a specific query or field of interest. For example, the results you receive after typing an inquiry into Google Search.
Information extraction: This is the process of extracting key information from unstructured or semi-structured data. For example, the information that gets extracted from an email message in order for a new event to be added to your calendar.
Clustering: This method groups similar objects into the same cluster. For example, streaming services use clustering analysis to group content into similar categories in order to provide users with recommendations based on their viewing history.
Summarization: Text summarization is the process of identifying the most important information from a lengthy source in order to produce a coherent and fluent summary that includes only the most vital points.
Categorization: Text categorization refers to the process of categorizing text into organized groups based on its content. For example, email messages are categorized as either spam or non-spam.
Benefits of text mining
Every day, organizations generate vast amounts of unstructured data that is often not searchable, nor easily managed. By applying text mining methods, relevant information from that data can be organized and categorized in an efficient and cost-effective manner.
The result is access to highly valuable structured data that can be used to help in making business decisions, improving customer experiences, automating tasks and more.
What are the practical applications of text mining?
Perhaps the most common end use case of text mining is text categorization. Text mining would be the first step for building a model that can categorize text into specific domains, such as spam versus non-spam emails, or detecting explicit content. Document classification is another common type of text categorization, especially for sorting news articles into categories such as domestic, international, sports, and lifestyle.
Other applications of text mining include document summarization, and entity extraction for identifying people, places, organizations and other entities. You can also use it for sentiment analysis, to identify and extract subjective information from written natural language. Sentiment analysis is especially useful for businesses to detect what their customers are saying on internet forums and social media.
Challenges in text mining
When dealing with very large amounts of data, certain issues can inevitably arise. For example, training machine learning models to extract information from vast amounts of data can be a lengthy process, particularly when you take into consideration the amount of time it takes to preprocess the data. On a practical level, storing huge amounts of data can pose a problem for organizations, especially when you consider the constant barrage of incoming emails, social media comments, product reviews and more that they face every day. Another challenge is finding a representative text sample from a large amount of data that can be used for machine learning.
Further issues that can hinder machine learning models processing text equally well and efficiently include word ambiguity (apple the fruit compared to Apple the company) and datasets containing multiple languages.
Through a combination of specialized technology and our community of contributors working in all major languages and regions, we can support the most complex text mining projects. Get in touch to discover how we can support your machine learning model.