Sentiment Polarity Entropy Dataset and Supplementary Materials

This record contains the full set of datasets and supplementary materials used in the Sentiment Polarity Entropy Project. The collection includes the concordances extracted from Sketch Engine for adjectives, adverbs, nouns, and verbs, which serve as the empirical basis for the analysis. For each of these four lexical datasets, the repository also provides the corresponding trinary sentiment probabilities (POS / NEU / NEG) derived from the annotated concordances, as well as the computed polarity entropy values obtained from these distributions. We include the Orange workflow project Polaridad_Entropy_Representacion. All Orange workflow files and related resources have been configured using the “import relative to workflow file” option. This ensures that all file paths are stored relative to the workflow’s location, making the project fully portable within the repository. As a result, anyone cloning or moving the repository will be able to open and run the workflows without manually adjusting file paths, improving reproducibility and simplifying collaboration. We include, as an example, a scatter plot displaying the results obtained from the adjectives corpus. Together, these files offer a complete and transparent account of the corpus evidence, sentiment modelling, and entropy computation procedures underlying the study. This dataset is directly linked to the software and scripts hosted in the associated GitHub project, archived as: Mir-Neira, E. (2025). eduard-mir/Sentiment-Polarity-Entropy: v1.2 (v1.2). Zenodo. https://doi.org/10.5281/zenodo.17768392. The GitHub repository contains the full computational pipeline, including corpus processing scripts, sentiment analysis code, and entropy computation procedures, whereas the present Zenodo record provides the corresponding datasets. To reference this project, please use the following citation: Mir-Neira, E. (2025). Sentiment Polarity Entropy Dataset and Supplementary Materials [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17768537