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Unauthorized use of programming tools such as Python, Selenium, webcrawlers, bots, etc. to scrape database search results or journal content is in violation of many of our licenses and can result in access being shutdown to the entire university.
Please contact firstname.lastname@example.org so that we can help you work with our vendors and to ensure what you are planning to do with potentially copyrighted texts complies with legal standards, including the publishing of your results.
This guide contains information (but not legal advice) about aspects of copyright most commonly encountered by the students, faculty, or staff of institutions of higher education.
Getting Started with Text Mining
What is your research question or your research goals?
What texts will address your research needs?
Identify and locate the text to be mined.
Consider the format of the text. Is the text in machine-readable format? Is the text high quality or does it need to be cleaned up?
Acquire the text via an API, authorized bulk downloading, or using platform provided or approved tools.
Mine the text and extract structured data. Apply text mining algorithms to the source text.
Build concept and category models. Identify the key concepts and/or create categories. The number of concepts returned from the unstructured data is typically very large. Identify the best concepts and categories for scoring.
Analyze the structured data. Employ data mining methods, such as clustering, classification, and predictive modeling, to discover relationships between the concepts or predict future patterns.