Brainstorming Minimal Features vs. Advanced features

When you're building specialized models, one of your most critical decisions is choosing which features to label. The right features influence both your model’s performance and your project’s timeline and budget. This lesson explains how to start with minimal features, progress to advanced ones, and adopt an iterative approach to find the right balance.

Step 1: Starting Simple with Minimal Features

Why Minimal Features?

Quick to Implement: Minimal features rely on straightforward metrics (e.g., text length, counts).
Low Cost: Both labeling time and computational requirements remain minimal.
Objective: Reduces ambiguity, since features like length or engagement counts are less prone to subjective interpretation.
Immediate Value: Provides a baseline for your project without heavy infrastructure or advanced modeling techniques.

Examples of Minimal Features

Text Length Analysis
A quick way to categorize text for initial insights:

def categorize_text_length(text):
    length = len(text)
    if length < 500:
        return "Short"
    elif length < 1500:
        return "Medium"
    else:
        return "Long"

# Usage
sample = "This is a sample post."
print(categorize_text_length(sample))  # outputs "Short"

Emoji Frequency
Emojis often convey sentiment or formality:

import re

def analyze_emoji_frequency(text):
    # Simplified emoji pattern
    emoji_pattern = re.compile("[\U0001F600-\U0001F64F]+", flags=re.UNICODE)
    emoji_count = len(emoji_pattern.findall(text))
    total_chars = len(text)
    freq = emoji_count / total_chars if total_chars else 0

    if freq == 0:
        return "none"
    elif freq < 0.001:
        return "low"
    else:
        return "high"

# Usage
text_with_emojis = "I love this product! 😍"
print(analyze_emoji_frequency(text_with_emojis))  # e.g., "low"

Quick Wins

Fast Labeling: Simple numerical or categorical thresholds.
Low Computation: Works at scale without hefty infrastructure.
Immediate Insights: Great for early-phase analysis or baseline models.

Step 2: Progressing to Advanced Features

Why Advanced Features?

Depth & Nuance: Goes beyond surface-level stats (e.g., capturing context, tone, or narrative flow).
Greater Predictive Power: Often addresses complex tasks that minimal features can’t handle.
Rich Insights: Helps detect topic shifts, sentiment intensity, or rhetorical devices.

Examples of Advanced Features

Topic Transition with BERT
Identify how topics shift within a document:

from transformers import BertTokenizer, BertModel
from sklearn.metrics.pairwise import cosine_similarity
import torch

def analyze_topic_transitions(text):
    # Load model & tokenizer
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertModel.from_pretrained('bert-base-uncased')
    model.eval()

    # Split text into paragraphs
    paragraphs = [p.strip() for p in text.split('\n') if p.strip()]
    if len(paragraphs) < 2:
        return []

    # Compute embeddings
    embeddings = []
    for paragraph in paragraphs:
        inputs = tokenizer(paragraph, return_tensors='pt', truncation=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
        # Use [CLS] token as paragraph embedding
        embeddings.append(outputs.last_hidden_state[0, 0, :].numpy())

    # Compare consecutive paragraphs
    transitions = []
    for i in range(len(embeddings) - 1):
        sim = cosine_similarity(embeddings[i].reshape(1, -1),
                                embeddings[i+1].reshape(1, -1))[0][0]
        transitions.append(1 - sim)  # shift_score = 1 - similarity

    return transitions

Narrative Structure
Analyze flow (introduction, conflict, conclusion), pacing, or sentiment arcs:
- Flow: Introduction → Development → Conclusion
- Pacing: Sentence length, variance
- Sentiment Arc: How sentiment changes from start to finish
Formatting Style
Examine bullet points, dividers, paragraph lengths, and line breaks for style indicators (e.g., “List-oriented,” “Dense,” “Airy,” etc.).

Challenges of Advanced Features

Implementation Complexity: Requires advanced NLP or deep learning.
Computational Cost: Especially for Transformer-based models (e.g., BERT).
Subjectivity: Interpreting sentiment or style can be tricky, requiring domain expertise.
Data Requirements: May need more labeled data or specialized annotations.

Step 3: Adopting an Iterative Approach

Phase 1: Minimal Features Foundation

Implement simple features (length, emoji usage).
Establish performance baselines and spot obvious data patterns.
Identify limitations and knowledge gaps.

Phase 2: Targeted Enhancement

Add specific advanced features to address known shortcomings.
Evaluate each new feature’s impact on performance (e.g., F1 scores, accuracy).
Keep only those that significantly boost predictive power.

Phase 3: Continuous Refinement

Periodically reassess feature effectiveness as your data or domain evolves.
Remove features with diminishing returns (high cost, low benefit).
Incorporate new features only when justified by performance or domain needs.

Putting It All Together: A Practical Example

Below is a simplified pipeline that can selectively apply minimal or advanced features:

def extract_features(text, use_advanced=False):
    """Extract minimal features, plus optional advanced features."""
    features = {}

    # Minimal Features
    features['length'] = len(text)
    features['length_category'] = categorize_text_length(text)
    features['emoji_usage'] = analyze_emoji_frequency(text)

    if use_advanced:
        # Example advanced feature: BERT-based topic transitions (for longer texts)
        if len(text) > 500:
            transitions = analyze_topic_transitions(text)
            features['topic_shift_score'] = max(transitions) if transitions else 0

        # Placeholder for additional advanced features (narrative style, formatting, etc.)

    return features

# Sample usage
sample_texts = [
    "Quick post with minimal content.",
    """Longer text with multiple paragraphs...
    This second paragraph might indicate a shift in topic.
    """
]

# Minimal processing
for txt in sample_texts:
    print(extract_features(txt, use_advanced=False))

# Advanced processing
for txt in sample_texts:
    print(extract_features(txt, use_advanced=True))

Key Takeaways

Start Simple: Minimal features (like text length, emoji usage) provide quick wins and an easy baseline.
Add Nuance Gradually: Introduce advanced features (topic transitions, narrative structure) to address specific predictive gaps or domain requirements.
Iterate and Evaluate: Keep monitoring which features deliver real value—drop what doesn’t work, keep improving what does.