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Financial Banking Dataset for Supervised Machine Learning Classification

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  • Irina RAICU

Abstract

Social media has opened new avenues and opportunities for financial banking institutions to improve the quality of their products and services and to understand and to adapt to their customers' needs. By directly analyzing the feedback of its customers, financial banking institutions can provide personalized products and services tailored to their customer needs. This paper presents a research framework for creation of a financial banking dataset in order to be used for Sentiment Classification using various Machine Learning methods and techniques. The dataset contains 2234 financial banking comments from Romanian financial banking social media collected via web scraping technique.

Suggested Citation

  • Irina RAICU, 2019. "Financial Banking Dataset for Supervised Machine Learning Classification," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(1), pages 37-49.
    • Handle: RePEc:aes:infoec:v:23:y:2019:i:1:p:37-49
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    References listed on IDEAS

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    1. Irina RAICU & Mirela Cătălina TÜRKEÚ, 2016. "An Opinion Mining And Sentiment Analysis Approach For Evaluating Customer Satisfaction In A Digital Banking Environment," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(18), pages 1-16.
    2. Ellie Birbeck & Dave Cliff, 2018. "Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals," Papers 1811.02886, arXiv.org.
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