This is a fully automated pipeline for fetching news articles, analysing their sentiment, and visualising insights. It leverages modern data engineering tools to create a streamlined workflow, making it an excellent example for data engineers and analysts looking to combine APIs, NLP, and cloud data warehousing. By focusing on five key categories: business, health, politics, science, and technology, this pipeline delivers targeted insights that aid decision-making in dynamic fields.
Why Current News Matters for Decision-Making
Staying informed with current news is essential for effective decision-making in an interconnected world. News provides real-time insights into events, trends, and shifts that shape personal, professional, and societal choices. For example, a sudden economic policy change might prompt a business to adjust strategies, or a health advisory could influence public behaviour. Without up-to-date information, decisions are misaligned with reality, leading to missed opportunities or increased risks.
def analyze_sentiment(text: str):
result = sentiment_pipeline(text)[0]
return {"label": result["label"], "score": float(result["score"])}
if __name__ == "__main__":
input_file = sys.argv[1]
output_file = sys.argv[2]
with open(input_file, "r") as f:
articles = json.load(f)
for article in articles:
content = article.get("description") or article.get("title", "")
sentiment = analyze_sentiment(content)
article["sentiment_label"] = sentiment["label"]
article["sentiment_score"] = sentiment["score"]
Sentiment analysis enhances this by quantifying news articles' emotional tone- positive, negative, or neutral. By revealing public perceptions and emotional undercurrents, it helps predict how news might impact decisions. For instance, negative sentiment in business news might signal caution for investors, while positive health news could encourage policy adoption. In the five categories this project targets:
Business: Sentiments guide investment, hiring, or expansion decisions. Positive earnings reports might drive stock purchases, while negative market outlooks could lead to diversification.
Health: Sentiments influence personal health choices and public policy. Negative tones in outbreak news might prompt stricter health measures, while positive vaccine news could boost public compliance.
Politics: Sentiments shape voter behaviour and policy advocacy. Negative public sentiment toward a policy could sway elections or spur activism.
Science: Sentiments affect research funding and adoption. Positive breakthrough news might accelerate investment, while ethical concerns could delay projects.
Technology: Sentiments shape startup strategies and tech adoption. For example, one of the articles with positive sentiment was a recent Business Insider article that highlights Andrew Ng’s view that AI has made coding faster, shifting the bottleneck to product management. Positive sentiments around AI’s efficiency might encourage startups to adopt AI tools for rapid prototyping. In contrast, concerns about product management challenges could push leaders to invest in stronger product teams or rely on intuitive decision-making to stay competitive.
The pipeline transforms raw news into actionable insights by analyzing sentiments in these categories, enabling proactive and informed decisions.
Highlight: Healthcare News and Its Impact
One of the articles was a study published on Medscape that highlights the long-term effects of SARS-CoV-2 infection on vascular ageing, particularly in women. The CARTESIAN study found that even mild COVID cases are linked to stiffer arteries, increasing cardiovascular risks equivalent to ageing arteries by about 5 years in women. This negative sentiment in health news has significant implications:
Individual Decisions: People, especially women, might prioritise cardiovascular screenings or lifestyle changes to mitigate risks.
Policy Decisions: Healthcare systems could allocate resources for long-term COVID monitoring or preventive care programs.
Research and Funding: Negative sentiment might drive funding for vascular health studies or treatments to address long-term COVID effects.
By capturing such health news and its sentiment, this pipeline helps stakeholders, from individuals to policymakers, make informed decisions to address emerging health risks.
Project Overview
The News Sentiment Analysis Pipeline automates the following steps:
- Fetching News Articles: Pulls articles from the GNews API across business, health, politics, science, and technology.
- Sentiment Analysis: Uses a pre-trained NLP model to classify article sentiments as positive, negative, or neutral.
- Data Storage: Loads processed data into Snowflake for structured storage.
- Visualisation: Generates insights via Snowflake dashboards, highlighting sentiment trends across categories.
- The pipeline is orchestrated using Apache Airflow, ensuring reliable scheduling and monitoring.
Conclusion
This pipeline demonstrates a modern data engineering workflow, with sentiment analysis providing actionable insights across business, health, politics, science, and technology. The recent healthcare news on SARS-CoV-2 and vascular ageing underscores the value of sentiment analysis in guiding health-related decisions.