Text Mining in Data Mining: Techniques and Applications
Text mining in data mining is the conversion of text from an unstructured state to a structured state to identify significant patterns and fresh insights. It is possible for companies to investigate and find hidden links within their unstructured data by utilizing advanced analytical approaches.
Data can be stored in databases in either of the 3 formats:
It is simpler to store and use this data for analytical operations and ML algorithms since it has been standardized into a tabular format with many columns and rows. Inputs like phone numbers, names, and addresses are common examples of structured data.
There is no standard data format for this data. It may contain text taken from reviews of products or social media platforms, as well as rich media formats including audio and video files.
This is a combination of unstructured and structured data, just as the name implies. It's somehow organized, but the structure is not enough to satisfy the criteria of a relational database. JSON, HTML and XML files are examples of semi-structured data.
Text Analytics Versus Text Mining
Although in everyday speech the phrases text mining and text analytics are mostly interchangeable, they can also indicate different things. Text mining and text analysis combine machine learning, statistics, and linguistics to find textual patterns and trends in unstructured data. Through text mining and analysis, more structured data may be created from the data, allowing for the discovery of more quantitative insights. Then, you can use data visualization techniques to share your findings with people.
Text Mining Strategies
Text mining is a method that comprises various steps that let you infer information from unstructured text data. The process of cleaning and converting text data into a usable format is called text preprocessing, and it must be done before you can use any of the various text mining techniques. Natural language processing (NLP) is a key component of this process, and it typically uses techniques like language identification, tokenization, part-of-speech tagging, chunking, and syntax parsing to properly prepare data for analysis. After text preprocessing is finished, text mining techniques can be used to extract insights from the data. Among these widespread text mining methods are:
Information retrieval (IR) produces relevant documents or information based on a pre-defined set of phrases or queries. Algorithms are used by IR systems to follow user activity and find pertinent information. Common applications of information retrieval include library catalogue systems and well-known search engines like Google. Following are some typical IR sub-tasks:
This is the process of dividing long-form material into phrases and words known as "tokens". These are then utilized in the models for text clustering and document matching tasks.
This refers to the practice of removing prefixes and suffixes from words to determine the base word form and meaning. This approach promotes information retrieval by lowering the size of indexing files.
Natural Language Processing (NLP)
Natural language processing, which evolved from computational linguistics, utilizes techniques from a variety of domains, including linguistics, computer science, artificial intelligence, and data science, to assist computers in comprehending human language in both written and vocal forms. Computers can "read" thanks to NLP sub-tasks that examine phrase structure and syntax. Below are some typical sub-tasks:
This technique creates a concise, in-depth summary of a document's key concepts.
Part-of-Speech (PoS) Tagging
With the use of the part-of-speech (PoS) tagging technique, each token in a document is assigned a tag based on the part of speech it represents, such as a noun, verb, or adjective to allow for a semantic analysis of the unstructured text.
Examining text documents and grouping them into specified topics or groups is what is referred to as text categorization, or text classification. Using this subtask simplifies the process of categorizing synonyms and abbreviations.
This activity allows you to monitor changes in consumer attitudes over time by detecting positive or negative sentiments from internal or external data sources. It is frequently used to present data on consumer perceptions about names, goods, and services. These insights can motivate companies to engage with customers and enhance workflows and user experiences.
When examining numerous documents, information extraction (IE) helps bring up the pertinent pieces of information. It also emphasizes the extraction of structured data from free text and the database storage of the extracted entities, characteristics, and relationship data. The following are typical information extraction sub-tasks:
Attribute selection: This involves choosing the significant characteristics (dimensions) that will contribute the most to the output of a predictive analytics model. The process is also known as feature selection.
Feature extraction: This approach is used to choose a subset of characteristics to boost the accuracy of a classification task. This is very important in terms of dimensionality reduction.
Named-entity recognition (NER): It's often referred to as entity identification or entity extraction and its goal is to locate and classify particular entities in text, such as names or locations. For instance, NER classifies "Mary" as a female name and "California" as a place.
This is the process of finding patterns and relevant insights from large data sets. It involves assessing both structured and unstructured data to uncover new information and it's frequently used in marketing and sales to examine consumer behavior. As text mining focuses on giving unstructured data structure and analyzing it to produce unique insights, it can be thought of as a subfield of data mining. Textual data analysis encompasses the aforementioned data mining techniques that were previously discussed.
Text Mining Applications
Text analytics has changed how many industries operate by enabling them to enhance the user experiences of their products and to make quicker and wiser business decisions. Examples of use cases are:
We ask our consumers for input on several occasions. Companies may quickly improve their customer experience when they use text analytics technologies in conjunction with feedback systems like chatbots, customer surveys, NPS (net-promoter scores), online reviews, support tickets, and social media profiles. Text mining and sentiment analysis can give organizations a way to rank the most important consumer pain areas, enabling them to respond to pressing problems immediately and boost customer satisfaction.
Text mining can be used in managing risk to track sentiment movements, extract data from whitepapers and analytical reports, and provide insights into market trends and industry developments. This information is particularly beneficial to banks and other financial institutions since it increases their level of trust when evaluating investments in diverse industries.
A detailed and comprehensive picture of how equipment and products operate can be provided through text mining. Text mining automates decision-making by progressively showing patterns that link to concerns and proactive and reactive maintenance strategies. Maintenance specialists can discover the core cause of issues and failures more quickly with the aid of text analytics.
Researchers in the biomedical area have found text mining to be useful, especially in clustering data. Health-related research is expensive and time-consuming to investigate manually but text mining offers an automated way of obtaining important data from medical publications.
Hackers frequently use spam as a means of entry to spread malware on computer systems. By filtering and excluding these emails from inboxes, text mining can enhance UX and reduce the possibility of cyberattacks for end users.
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