Text Data Management And Analysis A Practical Introduction To Information Retrieval And Text Mi
Gaynelle Theinert
Text mining, also known as text data mining, is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights. By applying advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, companies are able to explore and discover hidden relationships within their unstructured data.
Text Data Management And Analysis A Practical Introduction To Information Retrieval And Text Mi
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Since roughly 80% of data in the world resides in an unstructured format (link resides outside ibm.com), text mining is an extremely valuable practice within organizations. Text mining tools and natural language processing (NLP) techniques, like information extraction (PDF, 131 KB) (link reside outside of IBM), allow us to transform unstructured documents into a structured format to enable analysis and the generation of high-quality insights. This, in turn, improves the decision-making of organizations, leading to better business outcomes.
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.
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 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:
Information 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 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.
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.
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.
Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science[1] of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching.[2]
Depending on the application the data objects may be, for example, text documents, images,[3] audio,[4] mind maps[5] or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata.
The idea of using computers to search for relevant pieces of information was popularized in the article As We May Think by Vannevar Bush in 1945.[7] It would appear that Bush was inspired by patents for a 'statistical machine' - filed by Emanuel Goldberg in the 1920s and '30s - that searched for documents stored on film.[8] The first description of a computer searching for information was described by Holmstrom in 1948,[9] detailing an early mention of the Univac computer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy, Desk Set. In the 1960s, the first large information retrieval research group was formed by Gerard Salton at Cornell. By the 1970s several different retrieval techniques had been shown to perform well on small text corpora such as the Cranfield collection (several thousand documents).[7] Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s.
In 1992, the US Department of Defense along with the National Institute of Standards and Technology (NIST), cosponsored the Text Retrieval Conference (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods that scale to huge corpora. The introduction of web search engines has boosted the need for very large scale retrieval systems even further.
2. Information Retrieval In Libraries: Libraries were the first to adopt IR systems for information retrieval. In first-generation, it consisted, automation of previous technologies, and the search was based on author name and title. In the second generation, it included searching by subject heading, keywords, etc. In the third generation, it consisted of graphical interfaces, electronic forms, hypertext features, etc.
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