Poland makes an ideal laboratory for this kind of experiment. The country’s electricity system has historically been dominated by coal, and its ongoing shift toward renewables unfolds against a backdrop of volatile fossil fuel markets, tightening European Union emissions regulation, and the shockwaves of the 2022 energy crisis. Electricity prices in such a system are shaped by an intricate web of forces: the cost of natural gas and coal, the price of carbon allowances under the EU Emissions Trading System, broader financial conditions, and the steady drumbeat of regulatory announcements that tell traders what the rules of the game will be tomorrow.

The research team, Anna Manowska and Muhammad Waqar Hassan, built their analysis in two distinct natural-language-processing branches. The first used TF-IDF, a classic text-mining technique that measures how distinctive particular terms are within documents, to track the thematic prominence of 49 selected energy and climate-policy keywords across the corpus. The second applied VADER, a transparent rule-based sentiment scorer, directly to the cleaned article text to produce a daily measure of news tone, which the authors call the Climate Change News Index, or CCNI. Each article received a compound score between negative and positive one, and the daily index was calculated as the average of all scores published that day.

Sourcing the raw material required considerable care. The team used GDELT, the Global Database of Events, Language and Tone, to identify event-linked news URLs connected to European Union actors and energy developments. From an initial pool of 6,348 candidate documents, a sequential filtering cascade removed duplicates, records with unparsable dates, articles published before 2010, documents with empty or very short body text, and pieces that produced no VADER lexicon match. What remained was a final corpus of 5,378 articles, about 85 percent of the retrieved pool, concentrated heavily in English-language, Brussels-oriented policy journalism, with Politico Europe accounting for the predominant share.

That concentration matters. The authors are explicit that the CCNI reflects the tone of a selected European policy-news corpus, not the full European information environment and certainly not Polish public opinion. Polish-language media might emphasize domestic tariffs, household bills, mining employment, and national political debates rather than EU-level regulatory narratives, potentially generating different sentiment patterns altogether. The index is best understood as a proxy for the policy-news environment surrounding emissions regulation, fuel markets, and energy security, not as a measure of what Polish consumers or traders actually feel.

The descriptive findings are striking in their own right. The corpus skews positive on average across much of the sample, but mean tone declines sharply from early 2022 and stays subdued through 2023, coinciding with the European energy crisis. A bipolar keyword analysis revealed a clean thematic split: articles classified as positive clustered around forward-looking narratives of renewable deployment, energy transition strategies, and policy support mechanisms, while negative articles gravitated toward fuel price volatility, supply constraints, import dependence, and regulatory uncertainty. This structured separation suggests the sentiment classifications capture genuine differences in content rather than linguistic noise, though it does not prove the index improves forecasts.

To test that, the researchers aligned their text indicators with daily returns on Polish day-ahead electricity prices, TTF natural gas futures, Rotterdam coal futures, Brent oil, EUA carbon allowances, and the STOXX Europe 600 equity index. The Polish electricity series spanned 3,217 calendar days from January 2016 to October 2024, but after calendar alignment, lag construction, and complete-case filtering, the final econometric sample contained 1,011 observations. Two modeling approaches were compared: Ordinary Least Squares regression as an interpretable baseline, and Elastic Net, a regularized method combining LASSO and Ridge penalties that handles correlated predictors gracefully. Multicollinearity diagnostics confirmed the setup was sound, with all variance inflation factors below 1.08 in return space.

The forecasting design was deliberately rigorous. All predictions were made chronologically, with an expanding estimation window covering 448 one-day-ahead forecasts from late January 2023 to October 2024. Only information available by the forecast origin entered each model, preventing look-ahead bias. Elastic Net hyperparameters were re-selected at every refit through five-fold cross-validation confined to the estimation sample. Over the full evaluation, the complete Elastic Net specification achieved a mean absolute error of 55.57 PLN per megawatt-hour, a root mean squared error of 76.71 PLN/MWh, a mean absolute percentage error of 14.34 percent, and an R-squared of 0.622, delivering modest but statistically detectable improvement over a persistence benchmark.

But the crucial comparison isolates the text variables themselves. In a 50-observation sensitivity window, adding the news indicators reduced MAE by roughly 2.16 percent for both model types, from 22.64 to 22.15 for OLS and from 22.69 to 22.20 for Elastic Net. Yet RMSE actually worsened slightly in the same window, rising from 30.03 to 30.15 for OLS, meaning the text variables did not help with the largest errors, which matter most in spike-prone electricity markets. In the 100-observation window, all differences between full and market-control specifications fell below about 0.3 percent. Cross-correlations between the sentiment index and electricity returns at leads and lags of up to ten trading days remained below 0.003 in absolute value, offering no evidence of useful linear leading power in the daily specification.

The authors are careful about what these numbers mean. The study is observational, so the relationships are associative and predictive rather than causal. The framework also omits the physical variables that dominate real electricity-price forecasting, such as load forecasts, wind and solar generation, outages, congestion, and weather, so the text indicators are supplementary inputs rather than substitutes for operational modeling. No economic back-test of bidding, hedging, or trading value was performed, so the modest MAE improvement cannot yet be translated into monetary terms. Still, the workflow itself, from GDELT-assisted collection through transparent lexicon scoring to chronological out-of-sample validation, offers a reproducible template that other markets could adapt. Future work, the authors suggest, should compare VADER against domain-adapted transformer models like FinBERT, build separate Polish-language and EU-level indices, incorporate physical system variables, and pair conditional-mean forecasts with volatility models capable of handling the extreme spikes that define electricity markets. For now, the verdict is clear: the news whispers, but the fundamentals still shout.

Subject of Research: Sentiment analysis and market-data modeling of electricity price formation during Poland's energy transition

Article Title: Modeling electricity price formation during Poland’s energy transition using market data and sentiment analysis

Article References: Manowska, A., & Hassan, M. W. (2026). Modeling electricity price formation during Poland’s energy transition using market data and sentiment analysis. Frontiers in Energy Research, 14, Article 1780848. https://doi.org/10.3389/fenrg.2026.1780848

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1780848

Keywords: electricity prices, Poland, energy transition, sentiment analysis, VADER, TF-IDF, GDELT, EU Emissions Trading System, forecasting, natural gas, carbon allowances, Elastic Net