Free Text Mining: A Guidebook For The Social Sciences Ebooks Online
Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining: A Guidebook for the Social Sciences brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by sociologist Gabe Ignatow and computer scientist Rada Mihalcea, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.
Paperback: 208 pages
Publisher: SAGE Publications, Inc (May 25, 2016)
Language: English
ISBN-10: 148336934X
ISBN-13: 978-1483369341
Product Dimensions: 7.4 x 0.4 x 9.3 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
Best Sellers Rank: #701,474 in Books (See Top 100 in Books) #64 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing #404 in Books > Computers & Technology > Databases & Big Data > Data Mining #643 in Books > Politics & Social Sciences > Social Sciences > Methodology
If you are an undergraduate or graduate student in the social sciences, this is an excellent guide for the novice as well as the intermediate student in the area of text mining and analysis. You needn't be a programmer in order to apply the techniques in the book, as Ignatow directs you to open source or free trial tools that can be applied to specific projects and analyses. Particularly useful is the chapter on sentiment analysis which is a key research method used to study Twitter. The language is not highly technical for anyone with even the slightest grasp of technology and the Internet. Highly recommended as a resource for a research project or dissertation if you're doing webscraping and are afraid to code in R or Python.
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