Corporate disclosure became more descriptive rather than quantitative over time. Thus, textual analysis gained popularity in finance and business, however, it requires massive computing power. The paper presents the panel set of the raw frequencies of positive and negative words across 90,463 Forms 10-K filed at Security Exchange Commission (SEC) in EDGAR (the Electronic Data Gathering, Analysis, and Retrieval system) over the period 1995-2008. The dataset consists of 456 variables. The texts of the forms were retrieved from the SEC servers and processed using text mining techniques. The data relevant for archive analysis on the sentiment of the financial statements and financial reporting on SEC registrants. Potential reuse for creation of the tone or sentiments indexes. Long-time data series allows for dynamic analysis. The data set allows reducing the computer power requirements for further research.
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| http://dx.doi.org/10.1016/j.dib.2022.108110 | DOI Listing |
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