How do you define text mining?
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The process of text mining, sometimes referred by the name of text mining is the process of transforming unstructured data into structured formats in order to uncover meaningful patterns and create new insights. Utilizing advanced analytical methods that include Naive Bayes Support Vector Machines (SVM) and various other deep-learning algorithms, businesses are able to discover and explore the hidden connections in the unstructured information.
Text is one of the most commonly used types of data in databases. According to the database, this information can be classified as:
Data that is structured: The type of data is standardized into a tabular format that has multiple columns and rows that make it easy to process and store to analyze and machine learning algorithms. Structured data may contain inputs like addresses, names, and telephone numbers.
Non-structured information: Unstructured kind of data is not standardized in data format. It may contain text from sources such as social media or product reviews, as well as rich media formats, such as audio and video files.
Semi-structured data: as the title suggests, this type of data is a mixture of structured and unstructured formats of data. Although it does have some organization but it's not enough structure to satisfy the requirements of an underlying relational database. Examples of semi-structured information include XML, JSON and HTML files.
Text mining vs. text analytics
The words such as text mining, text mining, and text analytics, are generally synonymous in their meanings in conversations however they may also have different meanings. Text mining and analysis detects patterns in text and trends in unstructured data using the application of statistical analysis, machine learning and the study of linguistics. In transforming data into a structured format using text mining and analysis, more quantifiable information can be uncovered by studying text analytics. Techniques for visualization of data can be utilized to convey the findings to a larger audience.
Text mining techniques
The process of mining text involves a variety of activities that allow you to extract information from text that is not structured. Before you can employ various techniques for mining text data collection it is necessary to begin by preparing text data that is the process of cleansing and transforming text data into an usable format. This is an essential component of natural processing of language (NLP) and usually requires the use of methods like language identification tokenization of speech, part-of-speech tagging as well as syntax parsing, to structure the data properly to be analyzed. After the preprocessing of text is complete and you are able to apply techniques for mining text to gain insights from the text. A few of these text mining methods include:
Information retrieval
Information retrieval (IR) gives relevant documents or information in response to a pre-determined list of phrases or queries. IR systems employ algorithms to analyze user behavior and find relevant information. Information retrieval is a common feature within library catalog systems as well as popular search engines like Google. The most typical IR sub-tasks are:
Tokenization: It involves breaking down the long form text into phrases as well as words, which are referred to as "tokens". They are then utilized in models like the bag-of-words to help with text clustering and task of document matching.
Stemming: refers to the process of segregating suffixes and prefixes from words in order to determine the word's root in its form and the meaning. This technique enhances the retrieval of information by decreasing the size of the files that are indexed.
Natural processing of languages (NLP)
The process of natural language processing (NLP), which originated in computational linguistics utilizes techniques from a variety of disciplines, including artificial intelligence, computer science, linguistics, as well as data science, in order to enable computers to comprehend human language, both in written and oral forms. Through the analysis of grammar and sentence structure, NLP sub-tasks allow computers to "read". The most common sub-tasks are:
Summarization: This technique gives an overview of lengthy paragraphs of text in order to provide concise, cohesive outline of the document's key elements.
Part-of-Speech (PoS) tag: This method gives a tag to each element in a document, according to its part of speech--i.e. denoting nouns, verbs, adjectives, etc. This process allows semantic analysis of unstructured text.
Classification of text in the form of text: This process also referred to as text classification is the process of analyzing texts and classifying them according to predefined topics or categories. This task is especially useful when it comes to categorizing synonyms and abbreviations.
Sentiment: analysis determines whether a sentiment is positive or negative through external or internal sources of data, allowing you to observe the changes in attitudes of customers in time. It is often utilized to give information on opinions about brands or products and services. The insights gained from these surveys can enable companies to better connect with their customers and enhance the user experience and processes.
Information extraction
Information extraction (IE) exposes the most relevant bits of data that are needed when looking through for documents. It also helps in finding structured information in unstructured text and storing the entities, attributes and information about relationships in databases. The most common sub-tasks for information extraction are:
Feature selection: The process of feature selection also known as attribute selection is the procedure of identifying the key attributes (dimensions) that make the greatest contribution to the outcomes of the predictive analytics model.
Feature extraction: The process of selecting a subset of the features to increase the precision of an assignment of classification. This is crucial in reducing dimensionality.
Recognition of names (NER): is also called entity recognition or entity extraction is a method of identifying and categorizing particular entities in text for example, names or locations. For instance, NER identifies "California" as a place and "Mary" as the name of a woman.
Data mining
The term "data mining" refers to the practice of identifying patterns, and then extracting useful information from huge data sets. The process analyzes unstructured and structured data to discover new data, and it is often used to study consumer behavior in sales and marketing. Text mining is basically the sub-field of data mining since it focuses on providing order to unstructured data, and then analyzing it to produce new insights. The above mentioned techniques are examples of data mining, but are part of analysis of textual data.
Applications for mining text
The software for text analytics has affected the way in which various industries function by enabling them to improve customer experiences for their customers and also make quicker and more effective business decisions. Examples of applications are:
Service to customers: We have many ways we seek feedback from our customers. When coupled using text analytics software feedback systems, like chatbots, customer surveys NPS (net-promoter scores) online reviews, support tickets, as well as accounts on social networks, allow businesses to enhance their customer experience at a faster pace. The use of sentiment and text mining could help companies to prioritize the main issues for their customers. This allows them to resolve immediate issues and boost satisfaction with customers. Find out the ways Verizon utilizes the power of text analytics in its customer service.
Management of risk: text mining is a tool for risk management. It can offer insight into market trends in the financial sector and other industries by observing changes in sentiment as well as extracting data from analyst reports and whitepapers. This is especially valuable for banks as this information gives more confidence when evaluating the business investment options across different industries. Find out how CIBC and EquBot employ text analytics to reduce risk.
Maintenance: The use of text mining gives an extensive and comprehensive Image data collection of the workings and capabilities of machinery and products. As time passes text mining helps automate the process of making decisions by revealing patterns which are connected with issues and proactive and reactive maintenance processes. Text analytics aids maintenance specialists discover the root of failures and challenges faster. Find out the ways Korean Airlines is using text analytics to improve maintenance.
Health: Text mining techniques are increasingly beneficial to biomedical researchers. area, specifically for clustering data. The manual process of conducting research in medical fields is costly and time-consuming. Text mining is a method of automation to extract important information from medical literature.
Spam filters: Spam frequently serves as an opening for hackers to attack computer systems with malware. Text mining is the ability to block emails from inboxes improving the user experience, and minimizing the chance of cyber-attacks on users.
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