JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1195 10.6339/25-JDS1195 Data Science in Action Comparative Analysis of VADER and TextBlob on Financial News Headlines https://orcid.org/0000-0002-3227-5039 DahalKeshab Rajkeshabraj.dahal@cortland.edu1∗ GuptaAnkrit2 BudhathokiNirajan34 Department of Mathematics, State University of New York Cortland, Cortland, NY, USA Department of Computer Science, Central Michigan University, Mount Pleasant, MI, USA Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA Dept of Public Health Sciences, Henry Ford Health, Detroit, MI, USA Corresponding author. Email: keshabraj.dahal@cortland.edu. 2025107202500120 Supplementary Material

Python codes as well as datasets used in the study are available in a supplementary file.

14920242762025 2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2025 Open access article under the CC BY license.

Financial news headlines serve as a rich source of information on financial activities, offering a wealth of text that can provide insights into human behavior. One key analysis that can be conducted on this text is sentiment analysis. Despite extensive research over the years, sentiment analysis still faces challenges, particularly in handling internet slang, abbreviations, and emoticons commonly found on many websites that cover financial news headlines, including Bloomberg, Yahoo Finance, and Financial Times. This paper compares the performance of two sentiment analyzers—VADER and TextBlob—on financial news headlines from two countries: the USA (a well-developed economic nation) and Nepal (an underdeveloped economic nation). The collected headlines were manually classified into three categories (positive, negative, and neutral) from a financial perspective. The headlines were then cleaned and processed through the sentiment analyzers to compare their performance. The models’ performance is evaluated based on accuracy, sensitivity, specificity, and neutral specificity. Experimental results reveal that VADER performs better than TextBlob on both datasets. Additionally, both models perform better on financial news headlines from the USA than Nepal. These findings are further validated through statistical tests.

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