Office Hours Q&A: What Should We Consider As Noise in Feedback Analysis?

One of the most challenging aspects of feedback analysis is identifying and handling noise. In a recent office hours session, we explored this question: What should we consider as noise in feedback analysis, and what should be done to exclude it?

Defining Noise: A Context-Dependent Challenge

There's no single, universal definition of noise. What constitutes noise depends heavily on several factors: the type of data you're working with, its distribution, and your company's specific needs. However, from our experience, noise typically falls into three main categories.

The Three Categories of Noise

1. Data-Level Noise

The first type of noise appears in your raw data before you even begin analysis. This occurs when data arrives from various sources already containing inaccuracies.

Common examples include:

• Products assigned to the wrong SKU, category, or brand
• Reviews mistakenly linked to incorrect products
• Mismatched or mislabeled product information

These errors enter your system at the collection stage and can propagate throughout your analysis if not addressed early.

2. Analysis-Level Noise

Once you've collected and stored your data, you're ready to analyze it. This typically involves applying probabilistic models to extract insights. However, these models are never 100% accurate. They generate small percentages of inaccurate predictions, what we now commonly refer to in the world of generative AI as "hallucinations."

This category represents noise created during the analysis process itself, not inherent in the original data.

3. Task-Level Noise

The third category relates directly to your specific analytical objectives. Data that's valuable for one task may be noise for another.

Consider these scenarios:

Focusing on mid-range ratings: If you're analyzing reviews to understand nuanced feedback, extremely positive or negative reviews may not provide valuable insights. Users who are ecstatic or furious about a product don't represent the moderate majority. In this context, these extreme values become noise relative to your analytical goals.

Topic-specific analysis: Imagine you want to understand customer sentiment about a product's battery life. You publish a social media post asking for feedback on this specific feature. While many comments will address battery performance, others will discuss different product aspects entirely. You can't assume all responses are relevant to your battery analysis. Any feedback not discussing battery life is noise for this particular task.

Tailored Solutions for Different Noise Types

Since noise manifests in different forms, there's no one-size-fits-all solution. Each category requires its own strategic approach.

Addressing Data-Level Noise

The key is establishing robust standards before data enters your database. You need:

• A comprehensive knowledge base
• Clear data collection schemas
• Standardized extraction processes

By implementing these guidelines, you can significantly reduce noise during the collection phase. This is a fundamental requirement we've implemented at One Overflow.

Tackling Analysis-Level Noise

While using sophisticated models is important, it's not the complete solution. How you design your analytical approach matters equally.

Hallucinations are a well-known challenge with generative AI models. In our experience, they're most likely to occur when you run a generative AI model on unstructured data (like a collection of reviews) and ask for an unstructured output (such as a summary or recommendation). These models rely on creativity to produce well-written insights, and creativity increases susceptibility to hallucination.

A better approach: Structure your solution more deliberately. Instead of feeding raw reviews directly to the model:

1. Aggregate and pre-process the data
2. Perform preliminary calculations
3. Present already-structured information to the model
4. Guide the model's output format

When the model reasons over structured data and produces structured outputs, the extraction process becomes more controlled. This design philosophy has significantly reduced hallucinations in our experience, thereby minimizing analysis-level noise.

Managing Task-Level Noise

This is fundamentally an outlier detection and information retrieval problem, and fortunately, there are established solutions:

For extreme values: Statistical models and machine learning techniques like clustering can help identify outliers. Once identified, you need to determine whether these outliers represent noise or valuable signals. Statistical methods can then help you remove genuine noise while preserving meaningful exceptions.

For relevance filtering: This is a classic information retrieval challenge. Decades of research (stretching back 30 to 40 years) have produced numerous techniques for identifying relevant data. We implement these proven methods to extract information that's truly useful for our specific analytical tasks.

The Bottom Line

Effectively managing noise in feedback analysis requires understanding that noise is contextual and multifaceted. By recognizing these three distinct categories and applying targeted strategies to each, you can significantly improve the quality and reliability of your insights.

The complexity of the problem demands a sophisticated response, but with the right frameworks and careful design, noise becomes manageable rather than overwhelming.

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