Sentiment 2.0: From Labels to Layers

In sentiment analysis, simplicity can often fall short. Traditional methods frequently reduce complex opinions into binary labels – positive or negative. Yet, real-world sentiments are rarely that straightforward.

Imagine reading a product review that says, “The camera quality is amazing, but the battery life is poor.” Is this a positive review? A negative one? Or something in between?

Photo by Volodymyr Hryshchenko on Unsplash

Traditional Sentiment Analysis: Limitations in Handling Complexity

Despite high accuracy on straightforward sentiment tasks, traditional sentiment analysis models like BERT struggle with nuanced opinions due to several limitations:

• Mixed Sentiments: Real-world texts often contain both positive and negative sentiments within the same document or sentence. Traditional models may average these sentiments, diluting the insights.

• Single-Layer Labeling: Assigning one label to an entire text oversimplifies complex feedback, missing critical details about various aspects.

• Lack of Interpretability: Transformer models, while powerful, tend to operate as “black boxes.” In applications where transparency is essential, such as healthcare feedback or product reviews, this lack of interpretability is a significant drawback.

To address these challenges, we can turn to gradual semantics, a structured approach inspired by argumentation theory, where each sentiment-bearing statement is assigned a strength, and relationships among statements help in deriving a nuanced overall sentiment.

Gradual Semantics: What It Brings to Sentiment Analysis

The concept of gradual semantics upgrades traditional sentiment analysis by treating opinions not as isolated labels but as part of a structured argument. This approach provides three main benefits:

1. Sentiment as a Spectrum: Each sentiment-bearing statement receives a score from -1 to 1, enabling a more accurate representation of both sentiment and intensity.

2. Structured Relationships: Statements interact, with each one either supporting or attacking the overall sentiment, producing a comprehensive, layered view.

3. Handling Incomplete Information: Gradual semantics accommodate partial information, allowing us to build sentiment incrementally and refine the analysis as more data arrives.

Building the Sentiment Model with Chat Completions API and GPT-4o

To implement gradual semantics, we need to break down the text, assign each sentiment-bearing statement a score, and define relationships among them. Here’s how to do it using OpenAI’s Chat Completions API with GPT-4o.

Step 1: Sentiment Extraction and Scoring with Chat Completions API

We start by using GPT-4o to analyze each statement individually, extracting a nuanced sentiment score. By structuring the API call with a system prompt and user prompt, we can guide the model’s understanding and ensure consistent results.

import openai

openai.api_key = "YOUR_API_KEY"
client = OpenAI

# Define the system prompt
system_prompt = """
You are an AI trained in nuanced sentiment analysis. 
For each statement, provide a sentiment score between -1 (very negative) and 1 (very positive), 
and add a brief explanation for the score based on context.
"""

# Define the user prompt with statements
statements = [
    "The screen quality is superb.",
    "The battery life is disappointing.",
    "The price is high but the performance justifies it."
]

user_prompt = "\n".join([f"Evaluate sentiment: ‘{s}’" for s in statements])

# Call the Chat Completions API with system and user prompts
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ],
    max_tokens=150,
    temperature=0.5
)

# Extract and print the responses
output = response[‘choices’][0][‘message’][‘content’]
print("Model Response:\n", output)

Here, each statement receives a score and a brief explanation, helping capture the sentiment’s depth and context.

Step 2: Structuring Sentiments in an Argument Graph

Using NetworkX, we can organize each statement as a node, with edges representing relationships. This allows for a structured model where sentiment interactions (support or attack) influence the overall perception.

import networkx as nx

# Initialize a directed graph
G = nx.DiGraph()

# Add nodes with sentiment scores based on the API output
statements = {
    "The screen quality is superb.": 0.9,
    "The battery life is disappointing.": -0.7,
    "The price is high but the performance justifies it.": 0.5,
}

for statement, score in statements.items():
    G.add_node(statement, sentiment_score=score)

# Define relationships (support/attack)
G.add_edge("The screen quality is superb.", "Overall Sentiment", relation="support")
G.add_edge("The battery life is disappointing.", "Overall Sentiment", relation="attack")
G.add_edge("The price is high but the performance justifies it.", "Overall Sentiment", relation="support")

This graph-based approach enables us to analyze how individual sentiments interact and contribute to the overall perception, providing a multi-dimensional view of sentiment.

Step 3: Aggregating Sentiment with Gradual Semantics

Using gradual semantics, we calculate an overall sentiment score by aggregating individual scores based on their relationships. Supporting sentiments increase the score, while attacking sentiments reduce it, resulting in a nuanced final sentiment score.

def compute_final_sentiment(graph, target="Overall Sentiment"):
    support_score, attack_score = 0.0, 0.0
    
    for edge in graph.in_edges(target, data=True):
        source, _, data = edge
        score = graph.nodes[source]['sentiment_score']
        
        if data['relation'] == "support":
            support_score += score
        elif data['relation'] == "attack":
            attack_score += score
    
    final_score = support_score - abs(attack_score)
    return final_score

# Compute the overall sentiment for the text
final_sentiment_score = compute_final_sentiment(G)
print(f"Overall Sentiment Score: {final_sentiment_score}")

Real-World Applications and Benefits of Gradual Semantics

This structured sentiment approach is invaluable for real-world applications where nuanced interpretation is essential:

• Product Reviews: Gradual semantics can provide insight into specific aspects (e.g., camera quality, battery life) without losing the overall sentiment.

• Customer Feedback: Detailed feedback analysis helps identify exact areas for improvement.

• Social Media Monitoring: Structured sentiment analysis captures complex brand perception, essential for reputation management.

Why Gradual Semantics and LLMs Have the Potential to Outperform Traditional Sentiment Models

This approach offers several clear advantages over conventional sentiment analysis methods:

• Granular Interpretation: Each statement is independently assessed, providing a sentiment spectrum rather than a single score.

• Structured Reasoning: Argumentation graphs allow nuanced relationships, capturing complex interactions among statements.

• Explainable Results: Each sentiment score is accompanied by an explanation, making the model’s logic transparent and understandable.

Wrapping it up

Gradual semantics, supported by LLMs has the potential to elevate sentiment analysis from simple classification to a structured, explainable, and nuanced model. By integrating this approach, sentiment analysis can deliver actionable insights that reflect the full complexity of human opinion.

Whether in product reviews, customer feedback, or social media monitoring, gradual semantics has the potential to unlock a richer view of sentiment, enabling businesses and researchers to understand their audience at a deeper level.