Applied Text Mining in Python

Start Date: 11/29/2020

Course Type: Common Course

Course Link:

About Course

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Coursera Plus banner featuring three learners and university partner logos

Course Introduction

Applied Text Mining in Python This course teaches the full extent of the mining and processing capabilities of the Python programming language. It covers text mining, regular expressions, coding standards, and analyzing the resulting data. After completing this course, you will be able to: - Describe the state of the art in text mining and their applications - Design a program to perform text mining - Explain common patterns found in text mining - Encoding and preprocessing text - Mining for text - Manipulating text for text - Mining for machine-generated HTML - Networking with Python - Solve problems in Python - Mining for machine-generated HTML - NN (network-network) mining - Mining for machine-generated HTML - Regular expressions and search paths - Mining for text - Regular expressions and search paths - Mining for machine-generated HTML - Regular expressions and search paths - Mining for text - Regular expressions and search paths - Mining for machine-generated HTML - Regular expressions and search paths This is the fourth and last course in the Python 3 Programming Specialization.Welcome and Introduction Python Basics Strings, Quotes, and Symbols Functions, Quotes, and Local Variables Applying Analytics to Your Business This course will introduce you to the exciting field of analytics and help you apply its important functions to improve your business. You

Course Tag

Natural Language Toolkit (NLTK) Text Mining Python Programming Natural Language Processing

Related Wiki Topic

Article Example
Text mining Text mining is starting to be used in marketing as well, more specifically in analytical customer relationship management. Coussement and Van den Poel (2008) apply it to improve predictive analytics models for customer churn (customer attrition). Text mining is also being applied in stock returns prediction.
Text mining One online text mining application in the biomedical literature is PubGene that combines biomedical text mining with network visualization as an Internet service.
Text mining Labor-intensive manual text mining approaches first surfaced in the mid-1980s, but technological advances have enabled the field to advance during the past decade. Text mining is an interdisciplinary field that draws on information retrieval, data mining, machine learning, statistics, and computational linguistics. As most information (common estimates say over 80%) is currently stored as text, text mining is believed to have a high commercial potential value.
Text mining Text mining computer programs are available from many commercial and open source companies and sources. See List of text mining software.
Text mining Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling ("i.e.", learning relations between named entities).
Text mining Academic institutions have also become involved in the text mining initiative:
Text mining A range of text mining applications in the biomedical literature has been described.
Text mining Until recently, websites most often used text-based searches, which only found documents containing specific user-defined words or phrases. Now, through use of a semantic web, text mining can find content based on meaning and context (rather than just by a specific word). Additionally, text mining software can be used to build large dossiers of information about specific people and events. For example, large datasets based on data extracted from news reports can be built to facilitate social networks analysis or counter-intelligence. In effect, the text mining software may act in a capacity similar to an intelligence analyst or research librarian, albeit with a more limited scope of analysis. Text mining is also used in some email spam filters as a way of determining the characteristics of messages that are likely to be advertisements or other unwanted material. Text mining plays an important role in determining financial market sentiment.
Biomedical text mining There is an increasing interest in text mining and information extraction strategies applied to the biomedical and molecular biology literature due to the increasing number of electronically available publications stored in databases such as PubMed.
Text mining The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence.
Biomedical text mining Biomedical text mining (also known as BioNLP) refers to text mining applied to texts and literature of the biomedical and molecular biology domain. It is a rather recent research field on the edge of natural language processing, bioinformatics, medical informatics and computational linguistics.
Text mining Many text mining software packages are marketed for security applications, especially monitoring and analysis of online plain text sources such as Internet news, blogs, etc. for national security purposes. It is also involved in the study of text encryption/decryption.
Text mining By contrast to Europe, the flexible nature of US copyright law, and in particular fair use, means that text mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea, is viewed as being legal. As text mining is transformative, meaning that it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitisation project of in-copyright books was lawful, in part because of the transformative uses that the digitisation project displayed—one such use being text and data mining.
Text mining The issue of text mining is of importance to publishers who hold large databases of information needing indexing for retrieval. This is especially true in scientific disciplines, in which highly specific information is often contained within written text. Therefore, initiatives have been taken such as Nature's proposal for an Open Text Mining Interface (OTMI) and the National Institutes of Health's common Journal Publishing Document Type Definition (DTD) that would provide semantic cues to machines to answer specific queries contained within text without removing publisher barriers to public access.
Text mining Text mining methods and software is also being researched and developed by major firms, including IBM and Microsoft, to further automate the mining and analysis processes, and by different firms working in the area of search and indexing in general as a way to improve their results.
Text mining Because of a lack of flexibilities in European copyright and database law, the mining of in-copyright works (such as web mining) without the permission of the copyright owner is illegal. In the UK in 2014, on the recommendation of the Hargreaves review the government amended copyright law to allow text mining as a limitation and exception. It was only the second country in the world to do so, following Japan, which introduced a mining-specific exception in 2009. However, owing to the restriction of the Copyright Directive, the UK exception only allows content mining for non-commercial purposes. UK copyright law does not allow this provision to be overridden by contractual terms and conditions.
Text mining Yet as management information systems developed starting in the 1960s, and as BI emerged in the '80s and '90s as a software category and field of practice, the emphasis was on numerical data stored in relational databases. This is not surprising: text in "unstructured" documents is hard to process. The emergence of text analytics in its current form stems from a refocusing of research in the late 1990s from algorithm development to application, as described by Prof. Marti A. Hearst in the paper Untangling Text Data Mining:
Text mining Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods.
Text mining The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The fact that the focus on the solution to this legal issue was licences, and not limitations and exceptions to copyright law, led representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.
Biomedical text mining The main developments in this area have been related to the identification of biological entities (named entity recognition), such as protein and gene names as well as chemical compounds and drugs in free text, the association of gene clusters obtained by microarray experiments with the biological context provided by the corresponding literature, automatic extraction of protein interactions and associations of proteins to functional concepts (e.g. gene ontology terms). Even the extraction of kinetic parameters from text or the subcellular location of proteins have been addressed by information extraction and text mining technology. Information extraction and text mining methods have been explored to extract information related to biological processes and diseases.