The terms, text mining and text analytics, are largely synonymous in meaning in conversation, but they can have a more nuanced meaning. Text mining and text analysis identifies textual patterns and trends within unstructured data through the use of machine learning, statistics, and linguistics. By transforming the data into a more structured format through text mining and text analysis, more quantitative insights can be found through text analytics. Data visualization techniques can then be harnessed to communicate findings to wider audiences. Show Text mining techniques The process of text mining comprises several activities that enable you to deduce information from unstructured text data. Before you can apply different text mining techniques, you must start with text preprocessing, which is the practice of cleaning and transforming text data into a usable format. This practice is a core aspect of natural language processing (NLP) and it usually involves the use of techniques such as language identification, tokenization, part-of-speech tagging, chunking, and syntax parsing to format data appropriately for analysis. When text preprocessing is complete, you can apply text mining algorithms to derive insights from the data. Some of these common text mining techniques include: Information retrievalInformation retrieval (IR) returns relevant information or documents based on a pre-defined set of queries or phrases. IR systems utilize algorithms to track user behaviors and identify relevant data. Information retrieval is commonly used in library catalogue systems and popular search engines, like Google. Some common IR sub-tasks include:
Natural language processing (NLP)Natural language processing, which evolved from computational linguistics, uses methods from various disciplines, such as computer science, artificial intelligence, linguistics, and data science, to enable computers to understand human language in both written and verbal forms. By analyzing sentence structure and grammar, NLP sub-tasks allow computers to “read”. Common sub-tasks include:
Information extractionInformation extraction (IE) surfaces the relevant pieces of data when searching various documents. It also focuses on extracting structured information from free text and storing these entities, attributes, and relationship information in a database. Common information extraction sub-tasks include:
Data miningData mining is the process of identifying patterns and extracting useful insights from big data sets. This practice evaluates both structured and unstructured data to identify new information, and it is commonly utilized to analyze consumer behaviors within marketing and sales. Text mining is essentially a sub-field of data mining as it focuses on bringing structure to unstructured data and analyzing it to generate novel insights. The techniques mentioned above are forms of data mining but fall under the scope of textual data analysis. Text mining applications Text analytics software has impacted the way that many industries work, allowing them to improve product user experiences as well as make faster and better business decisions. Some use cases include: Customer service: There are various ways in which we solicit customer feedback from our users. When combined with text analytics tools, feedback systems, such as chatbots, customer surveys, NPS (net-promoter scores), online reviews, support tickets, and social media profiles, enable companies to improve their customer experience with speed. Text mining and sentiment analysis can provide a mechanism for companies to prioritize key pain points for their customers, allowing businesses to respond to urgent issues in real-time and increase customer satisfaction. Learn how Verizon is using text analytics in customer service. Risk management: Text mining also has applications in risk management, where it can provide insights around industry trends and financial markets by monitoring shifts in sentiment and by extracting information from analyst reports and whitepapers. This is particularly valuable to banking institutions as this data provides more confidence when considering business investments across various sectors. Learn how CIBC and EquBot are using text analytics for risk mitigation. Maintenance: Text mining provides a rich and complete picture of the operation and functionality of products and machinery. Over time, text mining automates decision making by revealing patterns that correlate with problems and preventive and reactive maintenance procedures. Text analytics helps maintenance professionals unearth the root cause of challenges and failures faster. Healthcare: Text mining techniques have been increasingly valuable to researchers in the biomedical field, particularly for clustering information. Manual investigation of medical research can be costly and time-consuming; text mining provides an automation method for extracting valuable information from medical literature. Spam filtering: Spam frequently serves as an entry point for hackers to infect computer systems with malware. Text mining can provide a method to filter and exclude these e-mails from inboxes, improving the overall user experience and minimizing the risk of cyber-attacks to end users. What is an analysis of a text?Text analysis is the process of using computer systems to read and understand human-written text for business insights. Text analysis software can independently classify, sort, and extract information from text to identify patterns, relationships, sentiments, and other actionable knowledge.
What is text analysis with example?Text analysis is really the process of distilling information and meaning from text. For example, this can be analyzing text written in reviews by customers on a retailer's website or analysing documentation to understand its purpose.
What are the steps in text analysis?There are 7 basic steps involved in preparing an unstructured text document for deeper analysis:. Language Identification.. Tokenization.. Sentence Breaking.. Part of Speech Tagging.. Chunking.. Syntax Parsing.. Sentence Chaining.. What are types of text analytics?Types of Text Analysis. Word frequency (lists of words and their frequencies) ... . Collocation (words commonly appearing near each other). Concordance (the contexts of a given word or set of words). N-grams (common two-, three-, etc.- word phrases). Entity recognition (identifying names, places, time periods, etc.). |