Comparative Analysis of VADER and TextBlob on Financial News Headlines

Dahal, Keshab Raj; Gupta, Ankrit; Budhathoki, Nirajan

doi:10.6339/25-JDS1195

Journal of Data Science

Comparative Analysis of VADER and TextBlob on Financial News Headlines

Keshab Raj Dahal

Ankrit Gupta Nirajan Budhathoki

https://doi.org/10.6339/25-JDS1195

Pub. online: 10 July 2025 Type: Data Science In Action

Open Access

Received
14 September 2024

Accepted
27 June 2025

Published
10 July 2025

Abstract

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.

Supplementary material

Supplementary Material

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

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2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

finance news sentiment analysis text mining

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This research received no external funding.

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