LLM for Sentiment Analysis

Sentiment analysis (also called opinion mining) is the computational study of opinions, sentiments, and emotions expressed in text. LLMs have achieved state-of-the-art performance by understanding context, nuance, and implicit sentiment.

Sentiment Analysis Types

Document-Level Sentiment

Sentence-Level Sentiment

Aspect-Based Sentiment

Emotion Detection

Mathematical Formulation

Classification-Based Sentiment

Aspect-Based Sentiment

Multi-Label Emotion Detection

LLM Approaches to Sentiment Analysis

Zero-Shot Classification

LLMs can classify sentiment without task-specific training by using prompt engineering.

Aspect-Based Analysis with LLMs

Emotion Detection

Evaluation Metrics

Accuracy and F1

Aspect-Based Metrics

Emotion Metrics

Practical Implementation

Basic Sentiment Analysis

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "meta-llama/Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

review = """The new iPhone has an incredible camera and the battery 
lasts all day, but the price is way too high for what you get."""

prompt = f"""Analyze the sentiment of the following review:
{review}

Provide:
1. Overall sentiment (positive/negative/neutral)
2. Key positive points
3. Key negative points

Analysis:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
analysis = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(analysis)

Aspect-Based Sentiment Analysis

def aspect_sentiment(text, aspects, model, tokenizer):
    aspects_str = ", ".join(aspects)
    prompt = f"""Analyze sentiment for each aspect in the following text:

Text: {text}
Aspects: {aspects_str}

Provide sentiment (positive/negative/neutral) for each aspect in JSON format:"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=150)
    result = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
    return json.loads(result)

# Example
text = "The hotel room was spacious and clean, but the breakfast was mediocre."
aspects = ["room", "breakfast"]
result = aspect_sentiment(text, aspects, model, tokenizer)
# {"room": "positive", "breakfast": "negative"}

Emotion Detection

EMOTIONS = ["joy", "sadness", "anger", "fear", "surprise", "disgust", "trust", "anticipation"]

def detect_emotions(text, model, tokenizer):
    prompt = f"""Detect emotions in the following text. 
Rate each emotion from 0-10:

Text: {text}

Emotions:"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=150)
    result = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
    return result

Advanced Techniques

Implicit Sentiment

LLMs excel at detecting implicit sentiment due to their world knowledge and contextual understanding.

Sarcasm Detection

Comparative Sentiment

Sentiment with Justifications

Domain-Specific Sentiment

Financial Sentiment

Financial sentiment analysis requires understanding market terminology and context.

Medical Sentiment

Medical sentiment analysis requires understanding clinical terminology and patient experiences.

Product Reviews

Product review sentiment often requires aspect-based analysis to capture feature-specific opinions.

Best Practices

Prompt Engineering

1. Clear instructions: Specify the sentiment categories and format
2. Context provision: Include relevant domain context
3. Examples: Provide example analyses for consistency
4. Nuance handling: Allow for mixed or complex sentiments

Quality Assurance

1. Inter-annotator agreement: Ensure consistent human labeling
2. Regular calibration: Validate against human judgments
3. Error analysis: Identify and address systematic errors
4. Domain adaptation: Fine-tune or prompt for specific domains

Practice Exercises

1. Comparison: Compare zero-shot and few-shot sentiment analysis on a benchmark dataset. How many examples are needed for competitive performance?

2. Aspect Extraction: Build an aspect extraction system for restaurant reviews. What aspects are most commonly discussed?

3. Sarcasm Detection: Analyze how LLMs handle sarcastic text. What patterns help detect sarcasm?

4. Domain Transfer: Evaluate sentiment analysis performance across domains (e.g., electronics, restaurants, movies). What domain shifts affect performance?

What to Learn Next