Part 3/9:
Handling thousands of reviews directly is computationally expensive and inefficient. To address this, the methodology involves:
Loading the dataset into a manageable format.
Filtering reviews pertaining to specific products—like various Fire tablets—by searching for keywords (e.g., "fire" and "tablet").
Randomly sampling a subset (e.g., 25 reviews) to examine representative feedback patterns, which is called an audit process.
This sampling helps gauge the typical length of combined review text (usually around 4,000–6,000 characters) and ensures prompts fed into GPT-3 stay within token limits.
Prompt Engineering and Summarization Strategies
The core of this approach is prompting GPT-3 to generate summaries of the reviews. Multiple prompt styles are experimented with: