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Project Objectives

Primary Research Goals

1. Sentiment-Engagement Relationship Analysis

Objective: Investigate whether article sentiment characteristics influence reader engagement on Wall Street Journal articles.

Specific Aims:

  • Determine if subjective vs. objective writing style correlates with comment volume
  • Analyze the relationship between positive/negative sentiment and reader engagement
  • Identify which sentiment metrics (VADER vs. TextBlob) provide better predictive power
  • Quantify the statistical significance of sentiment-engagement relationships

Success Metrics:

  • Statistical significance testing (p < 0.05)
  • Model performance evaluation (R², adjusted R²)
  • Identification of key sentiment variables driving engagement

2. Market Prediction Analysis

Objective: Explore whether WSJ article sentiment can predict S&P 500 market movements.

Specific Aims:

  • Test predictive power of same-day sentiment on same-day market returns
  • Analyze lagged effects (1-3 day prediction windows)
  • Compare different sentiment analysis approaches for market prediction
  • Control for market volume and other confounding variables

Success Metrics:

  • Statistical significance of sentiment variables in market prediction models
  • Model performance comparison across different time horizons
  • Identification of optimal sentiment indicators for market prediction

Technical Objectives

3. Data Collection & Processing

Objective: Build a robust web scraping and data processing pipeline.

Specific Aims:

  • Scrape comprehensive article metadata from WSJ archives
  • Implement reliable data cleaning and preprocessing workflows
  • Ensure data quality and consistency across the dataset
  • Create reproducible data collection processes

Success Metrics:

  • Complete dataset of 20,000+ articles
  • High data quality (minimal missing values, consistent formatting)
  • Reproducible scraping pipeline

4. Natural Language Processing Implementation

Objective: Apply state-of-the-art NLP techniques for sentiment analysis.

Specific Aims:

  • Implement VADER sentiment analysis for emotion intensity scoring
  • Apply TextBlob for polarity and subjectivity analysis
  • Compare and validate different NLP approaches
  • Create comprehensive sentiment feature engineering

Success Metrics:

  • Successful implementation of both VADER and TextBlob
  • Consistent sentiment scoring across the dataset
  • Validation of sentiment analysis accuracy

5. Statistical Analysis & Modeling

Objective: Conduct rigorous statistical analysis to test research hypotheses.

Specific Aims:

  • Perform linear regression analysis for engagement prediction
  • Implement time-series analysis for market prediction
  • Apply appropriate statistical tests and model validation
  • Control for confounding variables and bias

Success Metrics:

  • Properly specified statistical models
  • Appropriate handling of statistical assumptions
  • Clear interpretation of results and limitations

Methodological Objectives

6. Reproducible Research

Objective: Ensure all analysis is fully reproducible and well-documented.

Specific Aims:

  • Create clear documentation for all analysis steps
  • Implement version control for code and data
  • Provide detailed methodology descriptions
  • Share code and data where appropriate

Success Metrics:

  • Complete code documentation
  • Reproducible analysis notebooks
  • Clear methodology documentation

7. Interactive Visualization

Objective: Create user-friendly interfaces for exploring results.

Specific Aims:

  • Develop R Shiny dashboard for interactive analysis
  • Create visualizations for sentiment trends and patterns
  • Enable user exploration of specific articles and time periods
  • Provide intuitive data exploration tools

Success Metrics:

  • Functional interactive dashboard
  • Clear and informative visualizations
  • User-friendly interface design

Research Questions

Primary Questions

  1. Engagement Question: Does article sentiment (subjectivity, polarity, emotionality) significantly predict the number of comments posted on WSJ articles?

  2. Market Question: Can WSJ article sentiment on day t predict S&P 500 returns on day t + n (where n = 0, 1, 2, 3)?

Secondary Questions

  1. Sentiment Comparison: Which sentiment analysis approach (VADER vs. TextBlob) provides better predictive power for engagement and market movements?

  2. Temporal Patterns: Are there temporal patterns in sentiment that correlate with market volatility or reader engagement?

  3. Content Analysis: Do certain types of articles (by section, author, or topic) show stronger sentiment-engagement or sentiment-market relationships?

Expected Outcomes

Positive Outcomes

  • Identification of significant sentiment-engagement relationships
  • Discovery of predictive sentiment patterns for market movements
  • Development of robust NLP analysis pipeline
  • Creation of valuable dataset for future research

Potential Limitations

  • Limited predictive power due to market complexity
  • Potential confounding variables not controlled for
  • Temporal limitations of the dataset (2019-2020)
  • Possible selection bias in comment engagement

Success Criteria

Minimum Viable Results

  • Complete data collection and processing pipeline
  • Successful implementation of sentiment analysis
  • Statistical analysis of both research questions
  • Basic visualization and reporting of results

Optimal Results

  • Statistically significant findings for at least one research question
  • Strong model performance (R² > 0.1) for engagement prediction
  • Identification of actionable insights for content strategy
  • Publication-quality analysis and documentation

Timeline & Milestones

Phase 1: Data Collection (Weeks 1-2)

  • Set up web scraping infrastructure
  • Collect initial dataset
  • Implement data cleaning and preprocessing

Phase 2: NLP Implementation (Weeks 3-4)

  • Implement VADER and TextBlob analysis
  • Create sentiment feature engineering pipeline
  • Validate sentiment analysis results

Phase 3: Statistical Analysis (Weeks 5-6)

  • Conduct engagement prediction analysis
  • Perform market prediction analysis
  • Apply appropriate statistical tests

Phase 4: Visualization & Reporting (Weeks 7-8)

  • Develop R Shiny dashboard
  • Create comprehensive visualizations
  • Document findings and methodology
  • Prepare final report and presentation