000			04284nam a2200181Ia 4500
008			110101s9999||||xx |||||||||||||| ||und||
082			_aCEM TH-0401 _bDAS
100			_aDas, Srinjoy (PG190991) _990285
245		0	_aHarnessing online stakeholder interactions for analyzing public inconvenience caused by construction of indian metro rail projects (Softcopy is also available)
260			_c2021
300			_axix,143p.
505			_aTABLE OF CONTENTS ABSTRACT i UNDERTAKING v CERTIFICATE vii ACKNOWLEDGEMENTS ix NOMENCLATURE xi LIST OF FIGURES xvii LIST OF TABLES xix 1 CHAPTER-1: INTRODUCTION 3 1.1 Introduction 3 1.2 Objectives 4 1.3 Scope of the work 4 1.4 Methodology 4 2 CHAPTER-2: LITERATURE REVIEW 7 2.1 Overview 7 2.2 Public Inconvenience 7 2.3 Construction caused inconvenience 8 2.1 Public inconvenience caused by Metro Projects in India 13 2.2 Social Media as a database 16 2.3 Twitter as a preferred social medium for research 18 2.4 Advantages and Challenges of Social Media 19 2.5 Public participation using social media 20 2.6 Methodology 24 2.7 Metro Rail Agencies in India using social media platforms 24 2.8 Discussions and research position 25 3 CHAPTER-3: RESEARCH METHODOLOGY 27 3.1 Overview 27 3.2 Case Studies 27 3.3 Longitudinal Research and Sampling 28 3.4 Twitter Data 29 3.4.1 Data Extraction 30 3.4.2 Data Pre-processing 31 3.5 Content Analysis 32 3.6 Topic Modelling 32 3.7 Sentiment Analysis 33 3.8 Text Analytics 33 3.9 Social Network Analysis 34 4 CHAPTER-4: DATA COLLECTION 35 4.1 Overview 35 4.2 Developing Keywords for Sample 35 4.3 Extracting the data 37 4.4 Data De-duplication 38 4.5 Text Pre-processing 38 5 CHAPTER-5: DATA ANALYSIS AND RESULTS 41 5.1 Overview 41 5.2 Content Analysis of the Data 41 5.2.1 Usergroup Identification 41 5.3 Sentiment Analysis 42 5.4 Text Analytics and Topic Modeling 42 5.5 Social Network Analysis 43 5.6 Case Study 1 - Mumbai Metro 43 5.6.1 Inconvenience Categorization 43 5.6.2 User-groups Categorization 45 5.6.3 Sentiment Analysis 47 5.6.4 Text Analytics and Topic Modeling 50 5.6.5 Social Network Analysis 54 5.7 Case Study 2 – Pune Metro 57 5.7.1 Inconvenience Categorization 57 5.7.2 User-groups Categorization 59 5.7.3 Sentiment Analysis 61 5.7.4 Text Analytics and Topic Modeling 64 5.7.5 Social Network Analysis 68 5.8 Case Study 3- Bangalore Metro 72 5.8.1 Inconvenience Categorization 73 5.8.2 User-groups Categorization 75 5.8.3 Sentiment Analysis 77 5.8.4 Text Analytics and Topic Modeling 80 5.8.5 Social Network Analysis 83 5.9 Case Study 4 - Kolkata Metro 87 5.9.1 Inconvenience Categorization 87 5.9.2 User-groups Categorization 89 5.9.3 Sentiment Analysis 90 5.9.4 Text Analytics and Topic Modeling 94 5.9.5 Social Network Analysis 97 5.10 Semi-Structured Interviews 101 6 CHAPTER-6: CROSS – CASE ANALYSIS 105 6.1 Overview 105 6.2 Inconveniences Analysis Comparison 105 6.3 Sentiments Comparison 106 6.4 Stakeholders Comparison 107 6.5 Word Frequency Comparison 108 6.6 Social Network Analysis Comparison 110 7 CHAPTER-7: CONCLUSION AND RECOMMENDATION 115 7.1 Conclusion 115 7.2 Research Limitations 116 7.3 Future Scope 117 References 119 Annexure 1 – Inconvenience Parameters Comparison Table 125 Annexure 2 – Research Methodology of Twitter data across construction related papers 126 Annexure 3 – Research Methodology of Twitter data across non-construction related papers . 129 Annexure 4 – Different Research Methods applied on Twitter data across different papers ..... 132 Annexure 5 – Research Methodology 134 Annexure 6 – Semi-structured interview questions 135 Annexure 7 – Mumbai tweets examples 136 Annexure 8 – Pune tweets examples 138 Annexure 9 – Bangalore tweets examples 140 Annexure 10 – Kolkata tweets examples 142
700			_aDevkar, Ganesh (Guide)
890			_aIndia
891			_a2019 Batch
891			_aConstruction Engineering and Management
891			_aFT-PG
999			_c69579 _d69579