– Collected and processed 686 Reddit comments from the r/Economics subreddit using PRAW, specifically targeting posts containing “tariff” in headlines after January 1, 2025, focusing on public discourse regarding the U.S. 25% tariffs on Canadian imports.
– Implemented natural language processing and sentiment analysis using NLTK to classify public opinions into positive (highlighting manufacturing growth and investment opportunities), negative (focusing on recession fears and unemployment risks), and neutral categories, discovering that most users maintained a neutral stance.
– Optimized text preprocessing through paragraph splitting, regex pattern matching, punctuation removal, and stop-word filtering to enhance analysis quality; visualized findings with Matplotlib and word clouds, revealing high-frequency terms like “economy,” “recession,” “manufacturing,” and “market.”
– Identified specific public concerns (such as price increases and economic uncertainty) and translated these insights into actionable recommendations: businesses should adjust pricing strategies to remain competitive, governments should communicate clearly to prevent economic panic, and companies should diversify supply chains to reduce dependence on U.S. markets.
– Developed a comprehensive data pipeline in Python (using pandas, NumPy, NLTK, and seaborn) via Jupyter Notebook, saving CSV files at multiple stages to ensure transparency, and providing a framework for future research extensions, such as incorporating user location analysis or temporal sentiment tracking.
Skills: pandas · NumPy · Matplotlib · Seaborn
Author
shanghaizhangyijie@gmail.com
