| Description |
1 online resource (221 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- 1. Subjective and Social Well-Being -- 1.1. Introduction -- 1.1.1. Subjective Well-Being -- 1.1.2. Objective Measures -- 1.1.3. Multidimensional Indicators -- 1.1.4. Surveys -- 1.1.5. Social Networking Sites and Data at Scale -- 1.1.6. What You'll Find (and What You'll Not) in This Book -- 1.1.7. Wellbeing, Well Being or Well-Being? -- 1.2. Gross Domestic Product -- 1.3. Well-Being as a Multidimensional Notion -- 1.3.1. The Capability Approach |
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1.3.1.1. Empirical Limitations of the Capability Approach -- 1.3.2. Multidimensional Well-Being Indicators -- 1.3.2.1. HDI: Human Development Index -- 1.3.2.2. BLI: Better Life Index -- 1.3.2.3. HPI: Happy Planet Index -- 1.3.2.4. BES: Benessere Equo Sostenibile (Fair Sustainable Well-Being) -- 1.3.2.5. CIW: Canadian Index of Well-Being -- 1.3.2.6. Other Initiatives for Measuring Well-Being -- 1.3.2.7. GNH: Gross National Happiness -- 1.3.2.8. Pros and Cons of Multidimensional Indicators -- 1.4. Self-Reported Well-Being -- 1.4.1. Gallup Surveys -- 1.4.1.1. Gallup World Poll |
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1.4.1.2. Gallup-Sharecare and Global Well-Being Index -- 1.4.1.3. Well-Being Research Based on Gallup Data -- 1.4.2. European Social Survey -- 1.4.3. World Values Survey -- 1.4.4. European Quality of Life Survey -- 1.4.5. How to Collect (and Interpret) Self-Reported Evaluations -- 1.5. Social Networking Sites and Well-Being -- 1.5.1. Sentiment Analysis -- 1.5.2. Evaluating Subjective Well-Being on the Web -- 1.5.3. Pros and Cons of Large-Scale Data from SNS -- 1.5.4. International and Intercultural Comparisons -- 1.6. Subjective or Social Well-Being? -- 1.7. Glossary |
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2. Text and Sentiment Analysis -- 2.1. Text Analysis -- 2.1.1. Main Principles of Text Analysis -- 2.2. Different Types of Estimation and Targets -- 2.3. From Texts to Numbers: How Computers Crunch Documents -- 2.3.1. Modeling the Data Coming for Social Networks -- 2.4. Review of Unsupervised Methods -- 2.4.1. Scoring Methods: Wordfish, Wordscores and LLS -- 2.4.2. Continuous Space Word Representation: Word2Vec -- 2.4.3. Cluster Analysis -- 2.4.4. Topic Models -- 2.5. Review of Machine Learning Methods -- 2.5.1. Decision Trees and Random Forests -- 2.5.2. Support Vector Machines |
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2.5.3. Artificial Neural Networks -- 2.6. Estimation of Aggregated Distribution -- 2.6.1. The Need of Aggregated Estimation: Reversing the Point of View -- 2.6.2. The ReadMe Solution to the Inverse Problem -- 2.7. The iSA Algorithm -- 2.7.1. Main Advantages of iSA over the ReadMe Approach -- 2.8. The iSAX Algorithm for Sequential Sampling -- 2.9. Empirical Comparison of Machine Learning Methods -- 2.9.1. Confidence Intervals -- 2.10. Conclusions -- 2.11. Glossary -- 3. Extracting Subjective Well-Being from Textual Data -- 3.1. From SNS Data to Subjective Well-Being Indexes |
| Notes |
Description based upon print version of record |
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3.1.1. Pros & Cons of Twitter Data |
| Subject |
Social media.
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Internet marketing.
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Mass media.
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Communication.
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Sociology.
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Well-being.
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Social Media
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Sociology
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social media.
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mass media.
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sociology.
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comfort (sensation)
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Communication
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Internet marketing
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Mass media
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Social media
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Sociology
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Well-being
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| Form |
Electronic book
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| Author |
Porro, Giuseppe
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| ISBN |
9780429685835 |
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0429685831 |
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