Visit This Web URL https://masterytrail.com/product/accredited-expert-level-oracle-social-media-sentiment-analysis-advanced-video-course Lesson 1: Overview of Sentiment Analysis
1.1 Definition and Importance
1.2 Applications in Business
1.3 Historical Context
1.4 Key Terminologies
1.5 Types of Sentiment Analysis
1.6 Challenges in Sentiment Analysis
1.7 Tools and Technologies
1.8 Ethical Considerations
1.9 Case Studies
1.10 Future Trends
Lesson 2: Introduction to Oracle for Sentiment Analysis
2.1 Overview of Oracle
2.2 Oracle's Role in Data Analysis
2.3 Oracle Database Basics
2.4 Oracle Cloud Services
2.5 Oracle Machine Learning
2.6 Oracle Text
2.7 Oracle Data Miner
2.8 Oracle Big Data Services
2.9 Oracle and Social Media
2.10 Setting Up Oracle Environment
Lesson 3: Data Collection for Sentiment Analysis
3.1 Sources of Data
3.2 Data Collection Techniques
3.3 APIs for Social Media Data
3.4 Web Scraping Basics
3.5 Oracle Data Integrator
3.6 Data Storage Solutions
3.7 Data Privacy and Security
3.8 Legal Considerations
3.9 Data Quality and Cleaning
3.10 Tools for Data Collection
Lesson 4: Data Preprocessing
4.1 Importance of Data Preprocessing
4.2 Text Normalization
4.3 Tokenization
4.4 Stop Words Removal
4.5 Stemming and Lemmatization
4.6 Handling Emojis and Special Characters
4.7 Data Augmentation
4.8 Oracle Data Preparation
4.9 Data Transformation
4.10 Data Reduction Techniques
Module 2: Advanced Sentiment Analysis Techniques
Lesson 5: Basic Sentiment Analysis Models
5.1 Rule-Based Systems
5.2 Lexicon-Based Approaches
5.3 Machine Learning Basics
5.4 Supervised Learning
5.5 Unsupervised Learning
5.6 Hybrid Approaches
5.7 Oracle Machine Learning Models
5.8 Model Evaluation Metrics
5.9 Sentiment Analysis Libraries
5.10 Implementing Basic Models in Oracle
Lesson 6: Machine Learning for Sentiment Analysis
6.1 Introduction to Machine Learning
6.2 Feature Extraction
6.3 Training and Testing Data
6.4 Classification Algorithms
6.5 Regression Analysis
6.6 Clustering Techniques
6.7 Oracle Machine Learning Algorithms
6.8 Model Training in Oracle
6.9 Hyperparameter Tuning
6.10 Model Deployment
Lesson 7: Deep Learning for Sentiment Analysis
7.1 Introduction to Deep Learning
7.2 Neural Networks Basics
7.3 Recurrent Neural Networks (RNNs)
7.4 Long Short-Term Memory (LSTM)
7.5 Convolutional Neural Networks (CNNs)
7.6 Transformers and BERT
7.7 Oracle and Deep Learning
7.8 Implementing Deep Learning Models
7.9 Model Optimization
7.10 Case Studies in Deep Learning
Lesson 8: Natural Language Processing (NLP) Techniques
8.1 Introduction to NLP
8.2 Text Representation
8.3 Word Embeddings
8.4 Named Entity Recognition (NER)
8.5 Part-of-Speech Tagging
8.6 Syntax and Parsing
8.7 Oracle NLP Capabilities
8.8 Sentiment Analysis with NLP
8.9 Advanced NLP Techniques
8.10 NLP Libraries and Tools
Module 3: Oracle-Specific Tools and Applications
Lesson 9: Oracle Text and Sentiment Analysis
9.1 Introduction to Oracle Text
9.2 Oracle Text Features
9.3 Setting Up Oracle Text
9.4 Indexing and Searching
9.5 Sentiment Analysis with Oracle Text
9.6 Customizing Oracle Text
9.7 Oracle Text and Machine Learning
9.8 Oracle Text and NLP
9.9 Case Studies with Oracle Text
9.10 Best Practices
Lesson 10: Oracle Data Miner for Sentiment Analysis
10.1 Introduction to Oracle Data Miner
10.2 Setting Up Oracle Data Miner
10.3 Data Exploration
10.4 Data Transformation
10.5 Model Building
10.6 Model Evaluation
10.7 Sentiment Analysis Workflows
10.8 Oracle Data Miner and Machine Learning
10.9 Oracle Data Miner and NLP
10.10 Best Practices
Module 4: Practical Applications and Case Studies
Lesson 11: Real-Time Sentiment Analysis
11.1 Importance of Real-Time Analysis
11.2 Real-Time Data Collection
11.3 Real-Time Data Processing
11.4 Real-Time Model Deployment
11.5 Oracle Streaming Services
11.6 Real-Time Dashboards
11.7 Real-Time Alerts
11.8 Case Studies in Real-Time Analysis
11.9 Challenges in Real-Time Analysis
11.10 Best Practices
Lesson 12: Sentiment Analysis in Marketing
12.1 Role of Sentiment Analysis in Marketing
12.2 Customer Feedback Analysis
12.3 Brand Monitoring
12.4 Campaign Analysis
12.5 Competitor Analysis
12.6 Oracle Marketing Cloud
12.7 Sentiment Analysis Tools for Marketing
12.8 Case Studies in Marketing
12.9 Challenges in Marketing Analysis
12.10 Best Practices
Lesson 13: Sentiment Analysis in Customer Service
13.1 Role of Sentiment Analysis in Customer Service
13.2 Customer Feedback Analysis
13.3 Complaint Resolution
13.4 Customer Satisfaction Analysis
13.5 Oracle Service Cloud
13.6 Sentiment Analysis Tools for Customer Service
13.7 Case Studies in Customer Service
13.8 Challenges in Customer Service Analysis
13.9 Best Practices
13.10 Future Trends
Lesson 14: Sentiment Analysis in Finance
14.1 Role of Sentiment Analysis in Finance
14.2 Market Sentiment Analysis
14.3 Stock Price Prediction
14.4 Risk Management
14.5 Oracle Financial Services
14.6 Sentiment Analysis Tools for Finance
14.7 Case Studies in Finance
14.8 Challenges in Financial Analysis
14.9 Best Practices
14.10 Future Trends
Module 5: Advanced Topics and Future Directions
Lesson 15: Advanced Topics in Sentiment Analysis
15.1 Aspect-Based Sentiment Analysis
15.2 Cross-Lingual Sentiment Analysis
15.3 Multimodal Sentiment Analysis
15.4 Sentiment Analysis in Different Languages
15.5 Oracle and Advanced Sentiment Analysis
15.6 Advanced Tools and Techniques
15.7 Case Studies in Advanced Topics
15.8 Challenges in Advanced Topics
15.9 Best Practices
15.10 Future Directions
Lesson 16: Ethical Considerations in Sentiment Analysis
16.1 Importance of Ethics in Sentiment Analysis
16.2 Data Privacy and Security
16.3 Bias and Fairness
16.4 Transparency and Explainability
16.5 Legal Considerations
16.6 Oracle and Ethical Considerations
16.7 Ethical Guidelines and Standards
16.8 Case Studies in Ethical Considerations
16.9 Challenges in Ethical Considerations
16.10 Best Practices
Lesson 17: Future Trends in Sentiment Analysis
17.1 Emerging Technologies
17.2 AI and Machine Learning Advancements
17.3 Big Data and Sentiment Analysis
17.4 Oracle and Future Trends
17.5 Predictive Analytics
17.6 Sentiment Analysis in IoT
17.7 Sentiment Analysis in Healthcare
17.8 Sentiment Analysis in Education
17.9 Case Studies in Future Trends
17.10 Best Practices
Lesson 18: Building a Sentiment Analysis System with Oracle
18.1 System Architecture
18.2 Data Collection and Storage
18.3 Data Processing and Analysis
18.4 Model Building and Deployment
18.5 Oracle Tools and Services
18.6 Integration with Other Systems
18.7 Monitoring and Maintenance
18.8 Case Studies in System Building
18.9 Challenges in System Building
18.10 Best Practices
Lesson 19: Case Studies in Sentiment Analysis
19.1 Case Study 1: Retail Industry
19.2 Case Study 2: Healthcare Industry
19.3 Case Study 3: Finance Industry
19.4 Case Study 4: Education Industry
19.5 Case Study 5: Government Sector
19.6 Case Study 6: Technology Industry
19.7 Case Study 7: Hospitality Industry
19.8 Case Study 8: Entertainment Industry
19.9 Case Study 9: Manufacturing Industry
19.10 Case Study 10: Non-Profit Sector
Lesson 20: Best Practices in Sentiment Analysis
20.1 Data Quality and Cleaning
20.2 Model Selection and Evaluation
20.3 Ethical Considerations
20.4 Real-Time Analysis
20.5 Integration with Other Systems
20.6 Monitoring and Maintenance
20.7 Oracle Best Practices
20.8 Case Studies in Best Practices
20.9 Challenges in Best Practices
20.10 Future Directions
Module 6: Hands-On Labs and Projects
Lesson 21: Hands-On Lab 1: Data Collection and Preprocessing
21.1 Setting Up the Environment
21.2 Data Collection Techniques
21.3 Data Cleaning and Preprocessing
21.4 Oracle Data Integrator
21.5 Data Storage Solutions
21.6 Data Quality and Cleaning
21.7 Tools for Data Collection
21.8 Case Studies in Data Collection
21.9 Challenges in Data Collection
21.10 Best Practices
Lesson 22: Hands-On Lab 2: Basic Sentiment Analysis Models
22.1 Setting Up the Environment
22.2 Rule-Based Systems
22.3 Lexicon-Based Approaches
22.4 Machine Learning Basics
22.5 Supervised Learning
22.6 Unsupervised Learning
22.7 Hybrid Approaches
22.8 Oracle Machine Learning Models
22.9 Model Evaluation Metrics
22.10 Best Practices
Lesson 23: Hands-On Lab 3: Machine Learning for Sentiment Analysis
23.1 Setting Up the Environment
23.2 Feature Extraction
23.3 Training and Testing Data
23.4 Classification Algorithms
23.5 Regression Analysis
23.6 Clustering Techniques
23.7 Oracle Machine Learning Algorithms
23.8 Model Training in Oracle
23.9 Hyperparameter Tuning
23.10 Best Practices
Lesson 24: Hands-On Lab 4: Deep Learning for Sentiment Analysis
24.1 Setting Up the Environment
24.2 Neural Networks Basics
24.3 Recurrent Neural Networks (RNNs)
24.4 Long Short-Term Memory (LSTM)
24.5 Convolutional Neural Networks (CNNs)
24.6 Transformers and BERT
24.7 Oracle and Deep Learning
24.8 Implementing Deep Learning Models
24.9 Model Optimization
24.10 Best Practices
Lesson 25: Hands-On Lab 5: Natural Language Processing (NLP) Techniques
25.1 Setting Up the Environment
25.2 Text Representation
25.3 Word Embeddings
25.4 Named Entity Recognition (NER)
25.5 Part-of-Speech Tagging
25.6 Syntax and Parsing
25.7 Oracle NLP Capabilities
25.8 Sentiment Analysis with NLP
25.9 Advanced NLP Techniques
25.10 Best Practices
Lesson 26: Hands-On Lab 6: Oracle Text and Sentiment Analysis
26.1 Setting Up the Environment
26.2 Introduction to Oracle Text
26.3 Oracle Text Features
26.4 Setting Up Oracle Text
26.5 Indexing and Searching
26.6 Sentiment Analysis with Oracle Text
26.7 Customizing Oracle Text
26.8 Oracle Text and Machine Learning
26.9 Oracle Text and NLP
26.10 Best Practices
Lesson 27: Hands-On Lab 7: Oracle Data Miner for Sentiment Analysis
27.1 Setting Up the Environment
27.2 Introduction to Oracle Data Miner
27.3 Setting Up Oracle Data Miner
27.4 Data Exploration
27.5 Data Transformation
27.6 Model Building
27.7 Model Evaluation
27.8 Sentiment Analysis Workflows
27.9 Oracle Data Miner and Machine Learning
27.10 Best Practices
Lesson 28: Hands-On Lab 8: Real-Time Sentiment Analysis
28.1 Setting Up the Environment
28.2 Importance of Real-Time Analysis
28.3 Real-Time Data Collection
28.4 Real-Time Data Processing
28.5 Real-Time Model Deployment
28.6 Oracle Streaming Services
28.7 Real-Time Dashboards
28.8 Real-Time Alerts
28.9 Case Studies in Real-Time Analysis
28.10 Best Practices
Lesson 29: Hands-On Lab 9: Sentiment Analysis in Marketing
29.1 Setting Up the Environment
29.2 Role of Sentiment Analysis in Marketing
29.3 Customer Feedback Analysis
29.4 Brand Monitoring
29.5 Campaign Analysis
29.6 Competitor Analysis
29.7 Oracle Marketing Cloud
29.8 Sentiment Analysis Tools for Marketing
29.9 Case Studies in Marketing
29.10 Best Practices
Lesson 30: Hands-On Lab 10: Sentiment Analysis in Customer Service
30.1 Setting Up the Environment
30.2 Role of Sentiment Analysis in Customer Service
30.3 Customer Feedback Analysis
30.4 Complaint Resolution
30.5 Customer Satisfaction Analysis
30.6 Oracle Service Cloud
30.7 Sentiment Analysis Tools for Customer Service
30.8 Case Studies in Customer Service
30.9 Challenges in Customer Service Analysis
30.10 Best Practices
Module 7: Capstone Projects and Assessments
Lesson 31: Capstone Project 1: Building a Sentiment Analysis System
31.1 Project Overview
31.2 System Architecture
31.3 Data Collection and Storage
31.4 Data Processing and Analysis
31.5 Model Building and Deployment
31.6 Oracle Tools and Services
31.7 Integration with Other Systems
31.8 Monitoring and Maintenance
31.9 Case Studies in System Building
31.10 Best Practices
Lesson 32: Capstone Project 2: Sentiment Analysis in Marketing
32.1 Project Overview
32.2 Role of Sentiment Analysis in Marketing
32.3 Customer Feedback Analysis
32.4 Brand Monitoring
32.5 Campaign Analysis
32.6 Competitor Analysis
32.7 Oracle Marketing Cloud
32.8 Sentiment Analysis Tools for Marketing
32.9 Case Studies in Marketing
32.10 Best Practices
Lesson 33: Capstone Project 3: Sentiment Analysis in Customer Service
33.1 Project Overview
33.2 Role of Sentiment Analysis in Customer Service
33.3 Customer Feedback Analysis
33.4 Complaint Resolution
33.5 Customer Satisfaction Analysis
33.6 Oracle Service Cloud
33.7 Sentiment Analysis Tools for Customer Service
33.8 Case Studies in Customer Service
33.9 Challenges in Customer Service Analysis
33.10 Best Practices
Lesson 34: Capstone Project 4: Sentiment Analysis in Finance
34.1 Project Overview
34.2 Role of Sentiment Analysis in Finance
34.3 Market Sentiment Analysis
34.4 Stock Price Prediction
34.5 Risk Management
34.6 Oracle Financial Services
34.7 Sentiment Analysis Tools for Finance
34.8 Case Studies in Finance
34.9 Challenges in Financial Analysis
34.10 Best Practices
Lesson 35: Capstone Project 5: Advanced Topics in Sentiment Analysis
35.1 Project Overview
35.2 Aspect-Based Sentiment Analysis
35.3 Cross-Lingual Sentiment Analysis
35.4 Multimodal Sentiment Analysis
35.5 Sentiment Analysis in Different Languages
35.6 Oracle and Advanced Sentiment Analysis
35.7 Advanced Tools and Techniques
35.8 Case Studies in Advanced Topics
35.9 Challenges in Advanced Topics
35.10 Best Practices
Lesson 36: Capstone Project 6: Ethical Considerations in Sentiment Analysis
36.1 Project Overview
36.2 Importance of Ethics in Sentiment Analysis
36.3 Data Privacy and Security
36.4 Bias and Fairness
36.5 Transparency and Explainability
36.6 Legal Considerations
36.7 Oracle and Ethical Considerations
36.8 Ethical Guidelines and Standards
36.9 Case Studies in Ethical Considerations
36.10 Best Practices
Lesson 37: Capstone Project 7: Future Trends in Sentiment Analysis
37.1 Project Overview
37.2 Emerging Technologies
37.3 AI and Machine Learning Advancements
37.4 Big Data and Sentiment Analysis
37.5 Oracle and Future Trends
37.6 Predictive Analytics
37.7 Sentiment Analysis in IoT
37.8 Sentiment Analysis in Healthcare
37.9 Sentiment Analysis in Education
37.10 Best Practices
Lesson 38: Capstone Project 8: Building a Sentiment Analysis System with Oracle
38.1 Project Overview
38.2 System Architecture
38.3 Data Collection and Storage
38.4 Data Processing and Analysis
38.5 Model Building and Deployment
38.6 Oracle Tools and Services
38.7 Integration with Other Systems
38.8 Monitoring and Maintenance
38.9 Case Studies in System Building
38.10 Best Practices
Lesson 39: Capstone Project 9: Case Studies in Sentiment Analysis
39.1 Project Overview
39.2 Case Study 1: Retail Industry
39.3 Case Study 2: Healthcare Industry
39.4 Case Study 3: Finance Industry
39.5 Case Study 4: Education Industry
39.6 Case Study 5: Government Sector
39.7 Case Study 6: Technology Industry
39.8 Case Study 7: Hospitality Industry
39.9 Case Study 8: Entertainment Industry
39.10 Case Study 9: Manufacturing Industry
Lesson 40: Capstone Project 10: Best Practices in Sentiment Analysis
40.1 Project Overview
40.2 Data Quality and Cleaning
40.3 Model Selection and Evaluation
40.4 Ethical Considerations
40.5 Real-Time Analysis
40.6 Integration with Other Systems
40.7 Monitoring and Maintenance
40.8 Oracle Best Practices
40.9 Case Studies in Best Practices