Sentiment Analysis of Robotic Surgery System using Data from Website 'OpenAlex'

 

Sentiment Analysis of Robotic Surgery Research Using OpenAlex

Robotic surgery has become one of the most exciting advancements in modern medicine. Systems like the da Vinci, Versius, and Senhance promise greater precision, smaller incisions, and faster patient recovery. But how is the research community truly reacting to this technology? To find out, we can use two powerful open tools: Sentiment Analysis and OpenAlex.What is Sentiment Analysis?Sentiment Analysis is a branch of Natural Language Processing (NLP) that automatically determines whether a piece of text expresses a positive, negative, or neutral tone. In research and healthcare, it helps us understand overall academic and clinical opinion without manually reading hundreds of papers.What is OpenAlex?OpenAlex is a free, massive, open catalog of the world’s scientific literature. It indexes millions of research papers, authors, and citations. Its public API allows anyone to search and retrieve the latest academic publications — making it an excellent resource for real-time research intelligence.What Did We Find About Robotic Surgery Systems?We analyzed the 10 most recent research articles related to robotic surgery systems using OpenAlex and VADER sentiment analysis. 

Key Observations:

While many papers continue to highlight the advantages of robotic surgery — such as improved precision, better ergonomics for surgeons, and enhanced visualization — the overall sentiment in the latest research leans mixed to cautious.

Common Concerns Highlighted in Recent Papers:

• High acquisition and maintenance costs, limiting accessibility especially in developing countries and smaller hospitals.
• Steep learning curve for surgical teams, which can affect initial patient outcomes.
• Need for more long-term clinical data on safety, effectiveness, and cost-effectiveness.
• Technical limitations in certain complex procedures.

This reflects a healthy stage in the technology’s development: the initial hype is giving way to critical evaluation and calls for more rigorous evidence.Why This Approach Is ValuableCombining OpenAlex with Sentiment Analysis offers a fast, data-driven way to monitor how emerging medical technologies are perceived by the scientific community. It helps:

• Medical device companies identify improvement areas
• Hospitals make informed purchasing decisions
• Researchers spot gaps for future studies

As robotic surgery continues to evolve, keeping a pulse on research sentiment will be crucial for responsible innovation.

Python Code used

import requests
import pandas as pd
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from datetime import datetime

# Initialize Sentiment Analyzer
analyzer = SentimentIntensityAnalyzer()

def get_latest_robotic_surgery_articles(per_page=10):
    url = "https://api.openalex.org/works"
    
    params = {
        "search": "robotic surgery OR da vinci OR versius OR senhance OR robotic-assisted surgery",
        "per_page": per_page,
        "sort": "publication_date:desc",   # Latest first
        "filter": "type:article"
    }
    
    response = requests.get(url, params=params)
    
    if response.status_code != 200:
        print("API Error:", response.status_code)
        print(response.text)
        return None
    
    data = response.json()
    results = []
    
    for paper in data.get('results', []):
        abstract = paper.get('abstract') or "No abstract available"
        
        # Sentiment Analysis
        scores = analyzer.polarity_scores(abstract)
        compound = scores['compound']
        
        if compound >= 0.05:
            sentiment = "Positive"
        elif compound <= -0.05:
            sentiment = "Negative"
        else:
            sentiment = "Neutral"
        
        results.append({
            'OpenAlex_ID': paper.get('id', 'N/A').split('/')[-1],
            'Full_Title': paper.get('title', 'No title'),
            'Publication_Date': paper.get('publication_date'),
            'Year': paper.get('publication_year'),
            'Citations': paper.get('cited_by_count', 0),
            'Sentiment': sentiment,
            'Sentiment_Score': round(compound, 3),
            'Abstract': abstract[:380] + "..." if len(abstract) > 380 else abstract
        })
    
    df = pd.DataFrame(results)
    return df

# ========================
# Run Analysis
# ========================

print("🔍 Fetching the 10 Latest Articles on Robotic Surgery Systems...\n")

df = get_latest_robotic_surgery_articles(per_page=10)

if df is not None and not df.empty:
    # Display results
    pd.set_option('display.max_colwidth', None)
    display(df[['OpenAlex_ID', 'Full_Title', 'Publication_Date', 'Citations', 'Sentiment', 'Sentiment_Score']])
    
    print("\n" + "="*65)
    print("OVERALL SENTIMENT SUMMARY (Latest 10 Articles)")
    print("="*65)
    print(df['Sentiment'].value_counts())
    print(f"Average Sentiment Score: {df['Sentiment_Score'].mean():.3f}")
    
    # Optional: Save to CSV
    df.to_csv("latest_robotic_surgery_articles.csv", index=False)
    print("\n✅ Results saved to 'latest_robotic_surgery_articles.csv'")
else:
    print("No articles found.")