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Item #:
39156390
Text as Data: A New Framework for Machine Learning and the Social Sciences
87% of respondents would recommend this to a friend
Item #:
39156390
DKK 424
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What Stands Out
Interdisciplinary Approach
Integrates machine learning with social science methodologies, providing a comprehensive framework that enhances data interpretation and encourages innovative research across diverse fields.
Practical Applications
Offers real-world examples and case studies, making it easier for researchers to apply theoretical concepts to practical social data problems and derive actionable insights.
User-Friendly Framework
Presents a structured, accessible methodology that simplifies complex data analysis processes, enabling both novices and experts to effectively leverage machine learning in social science research.
Product Details
| Publisher | Princeton University Press |
| Publication date | March 29, 2022 |
| Language | English |
| Print length | 360 pages |
| ISBN-10 | 0691207550 |
| ISBN-13 | 978-0691207551 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 1 x 10 inches (17.8 x 2.5 x 25.4 cm) |
Who Should Buy?
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Social Scientists
Ideal for researchers in social sciences looking to utilize machine learning for data analysis and insights.
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Data Analysts
Perfect for professionals analyzing textual data, providing tools to extract valuable information from large datasets.
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Students
Beneficial for students studying data science or social sciences, offering foundational knowledge in machine learning applications.
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Casual Readers
Not suitable for those seeking light reading without prior knowledge of machine learning or social science concepts.
Product Description
Text as Data: A New Framework for Machine Learning and the Social Sciences
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Data Modeling & Design Editorial Review
The book "Text as Data: A New Framework for Machine Learning and the Social Sciences" offers a unique pragmatic approach to NLP and text analysis, particularly useful for social scientists and those interested in becoming a computational social scientist. The authors introduce a new framework to research, emphasizing the importance of splitting data between training and testing sets. While the book is easy to read and has a nice introduction, it lacks concrete technique implementations, making it challenging for newcomers to fully comprehend its implications. Additionally, certain chapters are written by different authors, resulting in an unbalanced approach to concept explanation. The book offers a practical solution-based perspective compared to most other technical-focused books, making it a favorite among those in the industry.
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Pros
- Practical solution-based perspective
- Offers a unique approach to NLP and text analysis
- Can be read easily
Cons
- Concrete implementation techniques are not presented well
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