| Chapter 1 | ||
| Fig. 1.1. | Sources of Big Data. | 5 |
| Chapter 3 | ||
| Fig. 3.1. | Design of Proposed System Flow Diagram. | 28 |
| Chapter 4 | ||
| Fig. 4.1. | Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Proposed System Flow Diagram. | 41 |
| Fig. 4.2. | Excellent Feature. | 46 |
| Fig. 4.3. | Clean Feature. | 47 |
| Fig. 4.4. | Comfortable Feature. | 47 |
| Fig. 4.5. | Friendly Feature. | 48 |
| Fig. 4.6. | Negative Feature. | 49 |
| Fig. 4.7. | Expensive Feature. | 49 |
| Fig. 4.8. | Small Feature. | 50 |
| Fig. 4.9. | Good Hotel Feature. | 51 |
| Fig. 4.10. | Hot Water Feature. | 52 |
| Fig. 4.11. | Helpful Staff Feature. | 52 |
| Fig. 4.12. | Good Location Feature. | 53 |
| Fig. 4.13. | Good Value Feature. | 54 |
| Fig. 4.14. | Features of Domestic New Delhi Hotels. | 55 |
| Fig. 4.15. | Features of Beijing Hotels. | 55 |
| Fig. 4.16. | Features of Chicago Hotels. | 56 |
| Fig. 4.17. | Features of Dubai Hotels. | 56 |
| Fig. 4.18. | Features of London Hotels. | 57 |
| Fig. 4.19. | International Tourism of Montreal Hotels. | 57 |
| Fig. 4.20. | Features of New York Hotels. | 58 |
| Fig. 4.21. | Features of San Francisco Hotels. | 58 |
| Fig. 4.22. | Features of Shanghai Hotels. | 59 |
| Fig. 4.23. | Features of Vegas Hotels. | 59 |
| Fig. 4.24. | Results of International Tourism Beijing, Shanghai, Las Vegas and Chicago Hotels. | 61 |
| Fig. 4.25. | Results of International Tourism Montreal, New York, New Delhi and San Francisco Hotels. | 62 |
| Fig. 4.26. | Results of International Tourism London and Dubai Hotels. | 63 |
| Chapter 5 | ||
| Fig. 5.1. | Pointwise Mutual Information Proposed System Flow Diagram. | 70 |
| Fig. 5.2. | Count of Beijing Features. | 74 |
| Fig. 5.3. | Count of Chicago Features. | 74 |
| Fig. 5.4. | Count of Dubai Features. | 75 |
| Fig. 5.5. | Count of Las Vegas Features. | 76 |
| Fig. 5.6. | Count of London Features. | 76 |
| Fig. 5.7. | Count of Montreal Features. | 77 |
| Fig. 5.8. | Count of New Delhi Features. | 78 |
| Fig. 5.9. | Count of San Francisco Features. | 78 |
| Fig. 5.10. | Count of Shanghai Features. | 79 |
| Fig. 5.11. | Count of Features and Cities. | 80 |
| Chapter 6 | ||
| Fig. 6.1. | Multiclass Multilabel Classification Proposed System Flow Chart. | 85 |
| Fig. 6.2. | Result of Beijing. | 91 |
| Fig. 6.3. | Result of Chicago. | 92 |
| Fig. 6.4. | Result of Dubai. | 92 |
| Fig. 6.5. | Result of Las Vegas. | 93 |
| Fig. 6.6. | Result of London. | 93 |
| Fig. 6.7. | Result of Montreal. | 94 |
| Fig. 6.8. | Result of New Delhi. | 94 |
| Fig. 6.9. | Result of New York. | 95 |
| Fig. 6.10. | Result of San Francisco. | 95 |
| Fig. 6.11. | Result of Shanghai. | 96 |
| Fig. 6.12. | Features of All Cities. | 99 |
| Chapter 7 | ||
| Fig. 7.1. | Loss Function of LDA Topic Modelling. | 105 |
| Fig. 7.2. | Loss Function of Doc2Vec. | 106 |
| Fig. 7.3. | Loss Function of TF-IDF Features. | 106 |
| Fig. 7.4. | Accuracy vs. Feature Type. | 107 |
| Fig. 1.1. | Sources of Big Data. | 5 |
| Fig. 3.1. | Design of Proposed System Flow Diagram. | 28 |
| Fig. 4.1. | Predicting Preferences of International and Domestic Tourists Using Association Rule Mining Proposed System Flow Diagram. | 41 |
| Fig. 4.2. | Excellent Feature. | 46 |
| Fig. 4.3. | Clean Feature. | 47 |
| Fig. 4.4. | Comfortable Feature. | 47 |
| Fig. 4.5. | Friendly Feature. | 48 |
| Fig. 4.6. | Negative Feature. | 49 |
| Fig. 4.7. | Expensive Feature. | 49 |
| Fig. 4.8. | Small Feature. | 50 |
| Fig. 4.9. | Good Hotel Feature. | 51 |
| Fig. 4.10. | Hot Water Feature. | 52 |
| Fig. 4.11. | Helpful Staff Feature. | 52 |
| Fig. 4.12. | Good Location Feature. | 53 |
| Fig. 4.13. | Good Value Feature. | 54 |
| Fig. 4.14. | Features of Domestic New Delhi Hotels. | 55 |
| Fig. 4.15. | Features of Beijing Hotels. | 55 |
| Fig. 4.16. | Features of Chicago Hotels. | 56 |
| Fig. 4.17. | Features of Dubai Hotels. | 56 |
| Fig. 4.18. | Features of London Hotels. | 57 |
| Fig. 4.19. | International Tourism of Montreal Hotels. | 57 |
| Fig. 4.20. | Features of New York Hotels. | 58 |
| Fig. 4.21. | Features of San Francisco Hotels. | 58 |
| Fig. 4.22. | Features of Shanghai Hotels. | 59 |
| Fig. 4.23. | Features of Vegas Hotels. | 59 |
| Fig. 4.24. | Results of International Tourism Beijing, Shanghai, Las Vegas and Chicago Hotels. | 61 |
| Fig. 4.25. | Results of International Tourism Montreal, New York, New Delhi and San Francisco Hotels. | 62 |
| Fig. 4.26. | Results of International Tourism London and Dubai Hotels. | 63 |
| Fig. 5.1. | Pointwise Mutual Information Proposed System Flow Diagram. | 70 |
| Fig. 5.2. | Count of Beijing Features. | 74 |
| Fig. 5.3. | Count of Chicago Features. | 74 |
| Fig. 5.4. | Count of Dubai Features. | 75 |
| Fig. 5.5. | Count of Las Vegas Features. | 76 |
| Fig. 5.6. | Count of London Features. | 76 |
| Fig. 5.7. | Count of Montreal Features. | 77 |
| Fig. 5.8. | Count of New Delhi Features. | 78 |
| Fig. 5.9. | Count of San Francisco Features. | 78 |
| Fig. 5.10. | Count of Shanghai Features. | 79 |
| Fig. 5.11. | Count of Features and Cities. | 80 |
| Fig. 6.1. | Multiclass Multilabel Classification Proposed System Flow Chart. | 85 |
| Fig. 6.2. | Result of Beijing. | 91 |
| Fig. 6.3. | Result of Chicago. | 92 |
| Fig. 6.4. | Result of Dubai. | 92 |
| Fig. 6.5. | Result of Las Vegas. | 93 |
| Fig. 6.6. | Result of London. | 93 |
| Fig. 6.7. | Result of Montreal. | 94 |
| Fig. 6.8. | Result of New Delhi. | 94 |
| Fig. 6.9. | Result of New York. | 95 |
| Fig. 6.10. | Result of San Francisco. | 95 |
| Fig. 6.11. | Result of Shanghai. | 96 |
| Fig. 6.12. | Features of All Cities. | 99 |
| Fig. 7.1. | Loss Function of LDA Topic Modelling. | 105 |
| Fig. 7.2. | Loss Function of Doc2Vec. | 106 |
| Fig. 7.3. | Loss Function of TF-IDF Features. | 106 |
| Fig. 7.4. | Accuracy vs. Feature Type. | 107 |
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