Sentiment Evaluation on WallStreetBets

Requirements Clarification & Assessment

• Objective of the Model:

  • Clarify whether the model aims to predict the sentiment of specific stocks, the overall sentiment of the subreddit, or another objective.
  • Determine if the sentiment analysis is binary (positive/negative) or multi-class (positive, negative, neutral, etc.).
  • Understand how the sentiment analysis results will be utilized, particularly if they will influence investment decisions.

• Data Requirements:

  • Ascertain access to historical data from the WallStreetBets subreddit.
  • Evaluate the availability, quality, and volume of data for training, validation, and testing.

• Performance Constraints:

  • Identify any specific performance benchmarks (e.g., accuracy, latency) the model must meet.
  • Determine the frequency of retraining and prediction (e.g., real-time vs. batch processing).

• Resources and Infrastructure:

  • Assess the computational resources required for developing, training, and deploying the model.
  • Consider the infrastructure needed for handling large volumes of data and ensuring model scalability.