Suchergebnisse

Inhaltsverzeichnis: About the authors -- Foundations -- Text mining and text analysis -- Acquiring data -- Research ethics -- The philosophy and logic of text mining -- Research design and basic tools -- Designing your research project -- Web scraping and crawling -- Text mining fundamentals -- Lexical resources -- Basic text processing -- Supervised learning -- Text analysis methods from the humanities and social sciences -- Analyzing narratives -- Analyzing themes -- Analyzing metaphors -- Text mining methods from computer science -- Text classification -- Opinion mining -- Information extraction -- Analyzing topics -- Writing and reporting your research -- Writing and reporting your research -- Appendix A. Data sources for text mining -- Appendix B. Text preparation and cleaning software -- Appendix C. General text analysis software -- Appendix D. Qualitative data analysis software -- Appendix E. Opinion mining software -- Appendix F. Concordance and keyword frequency software -- Appendix G. Visualization software -- Appendix H. List of websites -- Appendix I. Statistical tools -- Glossary -- References.

Presents the methodological tool kit for data collection (particularly questionnaire) design & development used at Statistics Netherlands (NCBS). The (re-)design of a continuous survey of living conditions is used to illustrate five development steps; (1) project preparation & risk analysis; (2) a qualitative study using exploratory tools in a questionnaire laboratory; (3) a qualitative field study; (4) a quantitative field pilot; & (5) implementation. Respondents are consulted throughout about design issues concerning the questionnaire & its administration. The model is meant as a flexible framework for deciding when to test what & by which tool. An eclectic classification of measurement error risks is proposed as a framework for describing risk hypotheses, which mainly serves to guarantee efficiency. Unanticipated metainformation is obtained in the process. 4 Tables, 6 References. Adapted from the source document.

From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding into computational techniques and seeing the bigger picture. With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis on key data collection and skills in Python