Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-watson-sentiment-analysis-advanced-video-course Lesson 1: Introduction to Advanced Sentiment Analysis
1.01 Understanding the evolution of sentiment analysis beyond basic positive/negative.
1.02 Exploring the limitations of traditional sentiment analysis approaches.
1.03 Introduction to IBM Watson Natural Language Understanding (NLU) and its role in sentiment analysis.
1.04 Overview of the Sentiment Analysis feature within Watson NLU.
1.05 Identifying the need for expert-level sentiment analysis techniques.
1.06 Understanding the different types of sentiment (e.g., emotional, attitudinal, evaluative).
1.07 Exploring the concept of granular sentiment analysis.
1.08 Introduction to the course objectives and learning outcomes.
1.09 Setting up the IBM Cloud environment for Watson NLU.
1.10 Brief demonstration of basic sentiment analysis using the Watson NLU interface.
Lesson 2: Deep Dive into Watson NLU Sentiment Analysis
2.01 Examining the core sentiment score and its interpretation.
2.02 Understanding the target-based sentiment analysis feature.
2.03 Exploring the role of keywords and entities in sentiment analysis.
2.04 Analyzing the sentiment of specific phrases and sentences.
2.05 Understanding the confidence score associated with sentiment predictions.
2.06 Exploring the different sentiment models available in Watson NLU.
2.07 Analyzing the impact of language on sentiment analysis accuracy.
2.08 Understanding the concept of sentiment polarity and intensity.
2.09 Using the Watson NLU API for programmatic sentiment analysis.
2.10 Troubleshooting common issues in basic sentiment analysis with Watson NLU.
Lesson 3: Handling Nuance and Irony
3.01 Identifying challenges in detecting irony and sarcasm.
3.02 Techniques for recognizing ironic language patterns.
3.03 Leveraging contextual information to infer sentiment in ironic statements.
3.04 Understanding how negation affects sentiment.
3.05 Analyzing the impact of intensifiers and mitigators on sentiment strength.
3.06 Exploring methods for handling subtle positive or negative sentiment.
3.07 Using custom models or dictionaries to improve irony detection.
3.08 Analyzing real-world examples of ironic sentiment in text.
3.09 Evaluating the performance of Watson NLU in handling irony.
3.10 Strategies for mitigating the impact of irony on overall sentiment scores.
Lesson 4: Aspect-Based Sentiment Analysis (ABSA)
4.01 Introduction to Aspect-Based Sentiment Analysis.
4.02 Understanding the importance of identifying specific aspects or features.
4.03 Using Watson NLU to extract entities and keywords as potential aspects.
4.04 Linking sentiment to specific aspects within a sentence.
4.05 Analyzing the sentiment towards different attributes of a product or service.
4.06 Exploring techniques for identifying implicit aspects.
4.07 Structuring data for effective ABSA using Watson NLU.
4.08 Visualizing aspect-based sentiment results.
4.09 Case studies demonstrating the application of ABSA.
4.10 Evaluating the accuracy of Watson NLU for aspect-based sentiment.
Lesson 5: Handling Subjectivity and Objectivity
5.01 Distinguishing between subjective opinions and objective statements.
5.02 Understanding the role of subjectivity in sentiment analysis.
5.03 Identifying subjective language patterns.
5.04 Using features to help differentiate subjective from objective text.
5.05 Analyzing the impact of objective statements on overall sentiment.
5.06 Techniques for filtering out objective content when focusing on sentiment.
5.07 Exploring the relationship between subjectivity and sentiment intensity.
5.08 Evaluating the ability of Watson NLU to identify subjective content.
5.09 Strategies for dealing with mixed subjective and objective content.
5.10 Real-world examples of analyzing subjectivity in reviews or social media.
Lesson 6: Sentiment Analysis of Emojis and Emoticons
6.01 Understanding the increasing importance of emojis in online communication.
6.02 Analyzing the sentiment conveyed by different emojis.
6.03 Techniques for preprocessing text containing emojis.
6.04 Integrating emoji sentiment into the overall sentiment score.
6.05 Using external libraries or resources for emoji sentiment analysis.
6.06 Handling variations in emoji usage and meaning.
6.07 Analyzing the impact of emoji combinations on sentiment.
6.08 Evaluating the performance of Watson NLU in handling emojis.
6.09 Strategies for mapping emojis to sentiment scores.
6.10 Case studies of analyzing sentiment in emoji-rich text.
Lesson 7: Advanced Preprocessing Techniques for Sentiment Analysis
7.01 Understanding the importance of text preprocessing for sentiment analysis.
7.02 Advanced tokenization and sentence boundary detection.
7.03 Handling noise, slang, and informal language.
7.04 Techniques for dealing with misspellings and typos.
7.05 Stemming and lemmatization and their impact on sentiment analysis.
7.06 Stop word removal and its considerations for sentiment.
7.07 Handling negation and its impact on word meaning.
7.08 Techniques for normalizing text for consistent analysis.
7.09 Using regular expressions for pattern matching in preprocessing.
7.10 Evaluating the impact of different preprocessing techniques on sentiment results.
Lesson 8: Leveraging Custom Models for Enhanced Sentiment Analysis
8.01 Introduction to custom models in Watson NLU.
8.02 Understanding the benefits of training custom sentiment models.
8.03 Identifying scenarios where custom models are necessary.
8.04 Preparing data for training custom sentiment models.
8.05 Annotating data for sentiment with specific labels.
8.06 Training a custom sentiment model using Watson NLU.
8.07 Evaluating the performance of a custom sentiment model.
8.08 Iterative refinement of custom sentiment models.
8.09 Deploying and utilizing custom sentiment models via the API.
8.10 Best practices for building and maintaining custom sentiment models.
Lesson 9: Sentiment Analysis in Different Languages
9.01 Understanding the challenges of multilingual sentiment analysis.
9.02 Exploring the language support for sentiment analysis in Watson NLU.
9.03 Techniques for preprocessing text in multiple languages.
9.04 Analyzing the impact of cultural nuances on sentiment.
9.05 Using language detection to route text to appropriate models.
9.06 Evaluating the performance of Watson NLU for sentiment in different languages.
9.07 Strategies for handling code-switching in sentiment analysis.
9.08 Leveraging translation services for sentiment analysis (with caveats).
9.09 Building custom sentiment models for specific languages.
9.10 Case studies of multilingual sentiment analysis projects.
Lesson 10: Sentiment Analysis of Social Media Data
10.01 Unique challenges of analyzing sentiment in social media.
10.02 Handling abbreviations, hashtags, and mentions.
10.03 Analyzing sentiment in short, informal text.
10.04 Identifying trending topics and their associated sentiment.
10.05 Using sentiment analysis for brand monitoring and reputation management.
10.06 Analyzing sentiment in customer feedback on social media.
10.07 Integrating social media data sources with Watson NLU.
10.08 Handling noise and irrelevant content in social media streams.
10.09 Ethical considerations in social media sentiment analysis.
10.10 Real-world examples of using Watson NLU for social media sentiment.
Lesson 11: Sentiment Analysis of Customer Reviews
11.01 Understanding the value of sentiment analysis in customer reviews.
11.02 Identifying key aspects mentioned in reviews (e.g., product features, service).
11.03 Analyzing the sentiment towards specific aspects in reviews.
11.04 Using sentiment analysis to identify customer pain points.
11.05 Analyzing the impact of positive and negative reviews on business.
11.06 Integrating review data from various platforms with Watson NLU.
11.07 Handling structured and unstructured review data.
11.08 Visualizing sentiment trends in customer reviews over time.
11.09 Developing action plans based on review sentiment insights.
11.10 Case studies of using Watson NLU for customer review analysis.
Lesson 12: Sentiment Analysis of News Articles and Headlines
12.01 Analyzing sentiment in journalistic writing.
12.02 Identifying the overall tone and bias in news articles.
12.03 Analyzing the sentiment towards specific entities mentioned in news.
12.04 Using sentiment analysis for media monitoring and analysis.
12.05 Tracking public opinion on current events through news sentiment.
12.06 Handling the formality and structure of news articles.
12.07 Analyzing sentiment in headlines and their impact.
12.08 Identifying potential misinformation through sentiment analysis.
12.09 Integrating news article sources with Watson NLU.
12.10 Real-world examples of using Watson NLU for news sentiment analysis.
Lesson 13: Sentiment Analysis of Survey Responses
13.01 Understanding the application of sentiment analysis to survey data.
13.02 Analyzing open-ended survey responses for sentiment insights.
13.03 Identifying common themes and their associated sentiment in surveys.
13.04 Using sentiment analysis to complement quantitative survey data.
13.05 Analyzing sentiment in different segments of survey respondents.
13.06 Handling the variability in survey response length and detail.
13.07 Integrating survey data platforms with Watson NLU.
13.08 Visualizing sentiment findings from survey responses.
13.09 Developing actionable insights from survey sentiment analysis.
13.10 Case studies of using Watson NLU for survey response analysis.
Lesson 14: Sentiment Analysis of Call Center Transcripts
14.01 Analyzing sentiment in spoken language transcripts.
14.02 Handling the challenges of transcribing and analyzing voice data.
14.03 Identifying customer emotions and satisfaction levels.
14.04 Using sentiment analysis to improve call center performance.
14.05 Analyzing agent performance based on customer sentiment.
14.06 Identifying key topics and issues raised in calls and their sentiment.
14.07 Integrating call center platforms with Watson NLU.
14.08 Handling speech disfluencies and background noise in transcripts.
14.09 Ensuring data privacy and security in transcript analysis.
14.10 Real-world examples of using Watson NLU for call center transcript sentiment.
Lesson 15: Sentiment Analysis of Chatbot Conversations
15.01 Analyzing sentiment in conversational text.
15.02 Understanding the nuances of chatbot interactions.
15.03 Identifying customer satisfaction and frustration during chatbot conversations.
15.04 Using sentiment analysis to improve chatbot performance and user experience.
15.05 Analyzing the impact of chatbot responses on customer sentiment.
15.06 Handling short, fragmented sentences in chatbot logs.
15.07 Integrating chatbot platforms with Watson NLU.
15.08 Identifying areas for chatbot improvement based on sentiment analysis.
15.09 Evaluating the effectiveness of chatbot interactions through sentiment.
15.10 Case studies of using Watson NLU for chatbot conversation sentiment.
Lesson 16: Sentiment Analysis for Market Research
16.01 Applying sentiment analysis in market research initiatives.
16.02 Analyzing consumer opinions and preferences.
16.03 Identifying market trends and emerging sentiment.
16.04 Using sentiment analysis for competitive intelligence.
16.05 Analyzing sentiment around new product launches.
16.06 Identifying target audience sentiment and preferences.
16.07 Integrating various data sources for comprehensive market sentiment analysis.
16.08 Visualizing market sentiment data for reporting.
16.09 Developing marketing strategies based on sentiment insights.
16.10 Real-world examples of using Watson NLU for market research sentiment.
Lesson 17: Sentiment Analysis for Brand Monitoring
17.01 Understanding the importance of monitoring brand sentiment.
17.02 Tracking mentions of a brand across various platforms.
17.03 Analyzing the overall sentiment towards a brand.
17.04 Identifying potential crises or negative sentiment spikes.
17.05 Using sentiment analysis for proactive reputation management.
17.06 Analyzing the impact of marketing campaigns on brand sentiment.
17.07 Integrating data from social media, news, and review sites.
17.08 Setting up alerts for significant changes in brand sentiment.
17.09 Reporting on brand sentiment to stakeholders.
17.10 Case studies of using Watson NLU for brand monitoring.
Lesson 18: Sentiment Analysis for Employee Feedback
18.01 Analyzing sentiment in internal employee feedback.
18.02 Identifying areas of employee satisfaction and dissatisfaction.
18.03 Using sentiment analysis to improve employee engagement.
18.04 Analyzing sentiment in internal communications and surveys.
18.05 Identifying potential issues or concerns within the organization.
18.06 Ensuring anonymity and privacy in employee feedback analysis.
18.07 Integrating internal communication platforms with Watson NLU.
18.08 Developing action plans based on employee sentiment insights.
18.09 Reporting on employee sentiment to HR and leadership.
18.10 Case studies of using Watson NLU for employee feedback sentiment.
Lesson 19: Sentiment Analysis for Political Analysis
19.01 Applying sentiment analysis to political discourse.
19.02 Analyzing public opinion on political figures and issues.
19.03 Identifying sentiment trends in political campaigns.
19.04 Using sentiment analysis for election forecasting (with caveats).
19.05 Analyzing sentiment in political speeches and debates.
19.06 Handling the complexity and subjectivity of political language.
19.07 Integrating data from news, social media, and political forums.
19.08 Identifying the sentiment of different demographic groups.
19.09 Ethical considerations in political sentiment analysis.
19.10 Real-world examples of using Watson NLU for political sentiment.
Lesson 20: Sentiment Analysis for Academic Research
20.01 Utilizing sentiment analysis in various academic disciplines.
20.02 Analyzing sentiment in literature, historical texts, and cultural artifacts.
20.03 Identifying sentiment trends in scholarly articles and research papers.
20.04 Using sentiment analysis to explore public opinion on research topics.
20.05 Analyzing sentiment in educational materials and student feedback.
20.06 Handling the diverse range of text types in academic research.
20.07 Integrating academic data sources with Watson NLU.
20.08 Presenting sentiment analysis findings in academic publications.
20.09 Collaborating with researchers on sentiment analysis projects.
20.10 Case studies of using Watson NLU for academic sentiment analysis.
Lesson 21: Advanced Feature Engineering for Sentiment Analysis
21.01 Going beyond basic word features for sentiment analysis.
21.02 Creating n-gram features for capturing word sequences.
21.03 Incorporating part-of-speech tagging for linguistic features.
21.04 Using dependency parsing to understand grammatical relationships.
21.05 Extracting semantic features using word embeddings.
21.06 Creating custom lexicons for specific domains or sentiment.
21.07 Utilizing external knowledge bases to enhance sentiment analysis.
21.08 Dimensionality reduction techniques for feature sets.
21.09 Evaluating the impact of different feature sets on sentiment performance.
21.10 Best practices for feature engineering in sentiment analysis.
Lesson 22: Evaluating and Measuring Sentiment Analysis Performance
22.01 Understanding the importance of evaluating sentiment analysis models.
22.02 Metrics for evaluating sentiment classification (accuracy, precision, recall, F1-score).
22.03 Metrics for evaluating sentiment intensity or scoring.
22.04 Using confusion matrices to analyze model errors.
22.05 Cross-validation techniques for robust evaluation.
22.06 Comparing the performance of different sentiment analysis models.
22.07 Benchmarking Watson NLU sentiment analysis against other approaches.
22.08 Analyzing the performance of aspect-based sentiment analysis.
22.09 Identifying areas for improvement based on evaluation metrics.
22.10 Reporting on sentiment analysis performance to stakeholders.
Lesson 23: Handling Ambiguity in Sentiment Analysis
23.01 Understanding the sources of ambiguity in sentiment analysis.
23.02 Identifying ambiguous language patterns.
23.03 Techniques for resolving ambiguity through context.
23.04 Using external knowledge to disambiguate sentiment.
23.05 Analyzing the impact of ambiguous statements on overall sentiment.
23.06 Strategies for flagging or highlighting potentially ambiguous sentiment.
23.07 Evaluating the ability of Watson NLU to handle ambiguity.
23.08 Techniques for human annotation to resolve ambiguity.
23.09 Iterative approaches to improving ambiguity handling.
23.10 Real-world examples of ambiguous sentiment and their resolution.
Lesson 24: Sentiment Analysis of Figurative Language
24.01 Understanding the challenges of analyzing figurative language (metaphors, similes).
24.02 Identifying figurative language patterns.
24.03 Techniques for interpreting the sentiment conveyed by figurative language.
24.04 Using contextual information to understand figurative meaning.
24.05 Analyzing the impact of figurative language on sentiment intensity.
24.06 Exploring methods for handling complex figurative expressions.
24.07 Evaluating the performance of Watson NLU in handling figurative language.
24.08 Strategies for mitigating the impact of misinterpreted figurative language.
24.09 Using custom models or dictionaries for figurative language analysis.
24.10 Real-world examples of analyzing sentiment in figurative language.
Lesson 25: Sentiment Analysis of Dialogue and Conversations
25.01 Analyzing sentiment in multi-turn conversations.
25.02 Understanding the flow of sentiment throughout a dialogue.
25.03 Identifying shifts in sentiment during a conversation.
25.04 Analyzing the sentiment of individual speakers in a dialogue.
25.05 Using conversational context to improve sentiment analysis.
25.06 Techniques for segmenting conversations for analysis.
25.07 Integrating dialogue data sources with Watson NLU.
25.08 Visualizing sentiment trends in conversations.
25.09 Identifying key moments of positive or negative sentiment in dialogues.
25.10 Case studies of using Watson NLU for dialogue sentiment analysis.
Lesson 26: Sentiment Analysis with Temporal Data
26.01 Analyzing sentiment over time.
26.02 Identifying sentiment trends and seasonality.
26.03 Using time series analysis for sentiment data.
26.04 Analyzing the impact of events on sentiment.
26.05 Identifying the duration and intensity of sentiment changes.
26.06 Integrating time-stamped data with Watson NLU.
26.07 Visualizing sentiment trends over different time periods.
26.08 Forecasting future sentiment based on historical data.
26.09 Developing strategies based on temporal sentiment insights.
26.10 Real-world examples of using Watson NLU for temporal sentiment analysis.
Lesson 27: Sentiment Analysis and Emotion Detection
27.01 Understanding the relationship between sentiment and emotion.
27.02 Exploring emotion detection capabilities in Watson NLU.
27.03 Analyzing the different emotions detected by Watson NLU.
27.04 Using emotion detection to complement sentiment analysis.
27.05 Identifying the intensity of different emotions.
27.06 Analyzing the co-occurrence of sentiment and emotions.
27.07 Interpreting the meaning of different emotional states.
27.08 Evaluating the performance of Watson NLU for emotion detection.
27.09 Case studies of using Watson NLU for combined sentiment and emotion analysis.
27.10 Best practices for utilizing emotion data alongside sentiment.
Lesson 28: Sentiment Analysis and Topic Modeling
28.01 Combining sentiment analysis with topic modeling.
28.02 Identifying the topics being discussed and their associated sentiment.
28.03 Using topic modeling to group sentiment by theme.
28.04 Analyzing the sentiment towards specific topics.
28.05 Identifying emerging topics and their initial sentiment.
28.06 Integrating topic modeling results with Watson NLU sentiment analysis.
28.07 Visualizing topics and their sentiment distribution.
28.08 Developing insights based on the intersection of topics and sentiment.
28.09 Using topic modeling to refine sentiment analysis results.
28.10 Case studies of using Watson NLU for combined sentiment and topic analysis.
Lesson 29: Sentiment Analysis and Entity Extraction
29.01 Combining sentiment analysis with entity extraction.
29.02 Analyzing the sentiment towards specific entities (people, organizations, locations).
29.03 Identifying the entities mentioned and their associated sentiment.
29.04 Using entity extraction to focus sentiment analysis on relevant subjects.
29.05 Analyzing the sentiment of relationships between entities.
29.06 Integrating entity extraction results with Watson NLU sentiment analysis.
29.07 Visualizing entities and their sentiment connections.
29.08 Developing insights based on the intersection of entities and sentiment.
29.09 Using entity extraction to improve aspect-based sentiment analysis.
29.10 Case studies of using Watson NLU for combined sentiment and entity analysis.
Lesson 30: Sentiment Analysis and Keyword Extraction
30.01 Combining sentiment analysis with keyword extraction.
30.02 Identifying the key terms being used and their associated sentiment.
30.03 Using keyword extraction to highlight important sentiment-bearing words.
30.04 Analyzing the sentiment of specific keywords and phrases.
30.05 Identifying trending keywords and their sentiment.
30.06 Integrating keyword extraction results with Watson NLU sentiment analysis.
30.07 Visualizing keywords and their sentiment distribution.
30.08 Developing insights based on the intersection of keywords and sentiment.
30.09 Using keyword extraction to refine sentiment analysis results.
30.10 Case studies of using Watson NLU for combined sentiment and keyword analysis.
Lesson 31: Integrating Watson Sentiment Analysis with Other Services
31.01 Integrating Watson NLU sentiment analysis with other IBM Cloud services.
31.02 Using Watson Discovery for ingesting and analyzing large datasets.
31.03 Integrating with Cloud Object Storage for data storage.
31.04 Using Cloud Functions for serverless sentiment analysis processing.
31.05 Integrating with databases for storing and querying sentiment results.
31.06 Using Watson Studio for building data pipelines and visualizations.
31.07 Integrating with business intelligence tools for reporting.
31.08 Exploring integrations with third-party data sources and applications.
31.09 Building end-to-end sentiment analysis workflows.
31.10 Best practices for integrating Watson NLU sentiment analysis.
Lesson 32: Scaling Sentiment Analysis Operations
32.01 Understanding the challenges of scaling sentiment analysis.
32.02 Designing scalable architectures for sentiment analysis pipelines.
32.03 Using the Watson NLU API for high-volume processing.
32.04 Implementing batch processing for large datasets.
32.05 Leveraging parallel processing for faster analysis.
32.06 Managing resource utilization and costs in scaled deployments.
32.07 Monitoring and optimizing the performance of sentiment analysis systems.
32.08 Handling data throughput and latency in real-time scenarios.
32.09 Strategies for distributing sentiment analysis workloads.
32.10 Case studies of scaling Watson NLU sentiment analysis.
Lesson 33: Monitoring and Maintaining Sentiment Analysis Models
33.01 Understanding the importance of monitoring sentiment analysis models.
33.02 Tracking model performance over time.
33.03 Identifying drift in sentiment data and model accuracy.
33.04 Setting up alerts for performance degradation.
33.05 Strategies for retraining or updating sentiment analysis models.
33.06 Monitoring the quality of input data for sentiment analysis.
33.07 Handling changes in language and slang over time.
33.08 Version control for sentiment analysis models and configurations.
33.09 Troubleshooting common issues in production sentiment analysis systems.
33.10 Best practices for maintaining sentiment analysis models.
Lesson 34: Ethical Considerations in Sentiment Analysis
34.01 Understanding the ethical implications of sentiment analysis.
34.02 Addressing bias in sentiment analysis models and data.
34.03 Ensuring fairness and inclusivity in sentiment analysis.
34.04 Protecting user privacy in sentiment data.
34.05 Handling sensitive topics and potentially harmful content.
34.06 Transparency in reporting sentiment analysis results.
34.07 Avoiding the misuse of sentiment analysis for manipulation or discrimination.
34.08 Developing ethical guidelines for sentiment analysis projects.
34.09 Compliance with data privacy regulations (e.g., GDPR, CCPA).
34.10 Case studies illustrating ethical dilemmas in sentiment analysis.
Lesson 35: Security Best Practices for Sentiment Analysis
35.01 Understanding the security risks associated with sentiment analysis.
35.02 Securing access to Watson NLU and related services.
35.03 Protecting sensitive sentiment data at rest and in transit.
35.04 Implementing authentication and authorization controls.
35.05 Monitoring for suspicious activity in sentiment analysis systems.
35.06 Handling data breaches and security incidents.
35.07 Ensuring compliance with industry security standards.
35.08 Best practices for secure coding when using the Watson NLU API.
35.09 Penetration testing and vulnerability assessments for sentiment analysis systems.
35.10 Case studies of security vulnerabilities in sentiment analysis.
Lesson 36: Advanced Visualization Techniques for Sentiment Analysis
36.01 Beyond basic charts for visualizing sentiment data.
36.02 Using word clouds to visualize sentiment-bearing terms.
36.03 Creating sentiment dashboards for real-time monitoring.
36.04 Visualizing aspect-based sentiment results.
36.05 Using network graphs to visualize relationships and sentiment.
36.06 Creating interactive visualizations for exploring sentiment data.
36.07 Using geospatial visualizations for location-based sentiment analysis.
36.08 Storytelling with sentiment data through visualizations.
36.09 Using visualization tools and libraries for sentiment analysis.
36.10 Best practices for creating impactful sentiment visualizations.
Lesson 37: Building a Real-World Sentiment Analysis Application
37.01 Designing the architecture of a sentiment analysis application.
37.02 Choosing the right tools and technologies for the application.
37.03 Developing the data ingestion and processing pipeline.
37.04 Integrating Watson NLU for sentiment analysis.
37.05 Building the user interface for displaying sentiment insights.
37.06 Implementing features for filtering and exploring sentiment data.
37.07 Adding reporting and alerting functionalities.
37.08 Deploying the sentiment analysis application on IBM Cloud.
37.09 Testing and debugging the application.
37.10 Best practices for building robust sentiment analysis applications.
Lesson 38: Optimizing Watson NLU Sentiment Analysis Performance
38.01 Identifying bottlenecks in sentiment analysis pipelines.
38.02 Optimizing API calls to Watson NLU.
38.03 Techniques for reducing processing time and latency.
38.04 Caching sentiment analysis results for frequently accessed data.
38.05 Using asynchronous processing for improved throughput.
38.06 Monitoring resource usage and optimizing resource allocation.
38.07 Benchmarking different configurations for optimal performance.
38.08 Troubleshooting performance issues in Watson NLU sentiment analysis.
38.09 Strategies for cost optimization in scaled deployments.
38.10 Case studies of optimizing Watson NLU sentiment analysis performance.
Lesson 39: Future Trends in Sentiment Analysis
39.01 Exploring emerging trends in natural language processing.
39.02 The role of deep learning in advanced sentiment analysis.
39.03 Sentiment analysis of multimodal data (text, image, video).
39.04 Analyzing sentiment in conversational AI and virtual assistants.
39.05 The future of aspect-based sentiment analysis.
39.06 Sentiment analysis in real-time and streaming data.
39.07 The impact of explainable AI on sentiment analysis.
39.08 Ethical considerations in future sentiment analysis developments.
39.09 New research directions in sentiment analysis.
39.10 Preparing for the future of sentiment analysis with Watson NLU.
Lesson 40: Expert-Level Sentiment Analysis Project
40.01 Defining the scope and objectives of an expert-level sentiment analysis project.
40.02 Selecting a real-world dataset for the project.
40.03 Applying advanced preprocessing techniques to the dataset.
40.04 Utilizing custom models and advanced features in Watson NLU.
40.05 Implementing aspect-based and temporal sentiment analysis.
40.06 Integrating with other services and scaling the solution.
40.07 Evaluating the performance of the sentiment analysis system.
40.08 Visualizing and presenting the sentiment analysis findings.
40.09 Addressing ethical and security considerations in the project.