Introduction/Objective: This study aims to develop an uncertainty indicator for Ecopetrol, one of the leading companies in Colombia’s energy sector, through sentiment analysis of news articles and financial reports. The research addresses a gap in the literature by combining sentiment analysis with energy sector-specific data.

Methodology: Using advanced Natural Language Processing (NLP) techniques and deep learning models such as BERT, the study seeks to quantify how market perception—reflected in textual data—affects the volatility of the company’s assets. Language models such as FinBERT, XLM-RoBERTa, BETO, and DeBERTa were used to analyze sentiment in multilingual texts (Spanish and English).

Results: Spikes in headline entropy (Spanish/English) aligned with key events, showing a correlation with Ecopetrol’s volatility. Sentiment analyses using FinBERT and XLM-RoBERTa stood out for their accuracy in financial texts and their ability to capture multilingual perceptions.

Conclusions: A negative relationship was identified between positive sentiment and volatility, suggesting that optimism reduces market uncertainty. The study demonstrates that AI-based sentiment analysis is a valuable tool for forecasting volatility in emerging markets.

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Keywords:
Uncertainty,
natural language processing,
BERT,
sentiment analysis,
financial volatility,
Ecopetrol.

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