Abstract
With the emergence and tremendous growth of text mining, a computer-assisted approach for capturing sentiment viewpoints from textual data is gradually becoming a promising field, particularly when researchers are increasingly facing the problem of filtering bunches of useless information without capturing the essence in the big data era. This study aims at observing and classifying the sentiment orientation in CEO letters, digging the main corporate social responsibility (CSR) themes, and examining the effectiveness of CEO letters’ sentiment on forecasting financial performance. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. The results of preliminary evaluations validate that approximately 62.14% of the texts represent positive polarity even when companies are not in a promising economic situation. The CSR themes mainly focus on business ethical responsibility, particularly ethical activities. Among various machine-learning approaches, the logistic regression approach is appropriate for predicting financial performance with the state-of-the-art accuracy of 70.46 %. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. This work not only offers a new analytic framework for associating linguistic theory with computer science and economic models but will also improve stakeholders’ decision-making.
Highlights
IntroductionE high-tech novel technique of sentiment analysis offers a more efficient and accurate way for text processing, and its amazing pace of innovation, low costs, and scalability make it a highly attractive and alternative approach
Combing the eleven categorizations with our 2016 financial performance, interestingly, we found that poorly performing companies are expected to use a more active language to describe and evaluate their corporate social responsibility (CSR). is phenomenon can be explained further by the impression management effect
CEO letter contains information about corporate social responsibility performance in corporate social responsibility report (CSRR), which is designated for stakeholders to make their investment decisions
Summary
E high-tech novel technique of sentiment analysis offers a more efficient and accurate way for text processing, and its amazing pace of innovation, low costs, and scalability make it a highly attractive and alternative approach. Corporate social responsibility report (CSRR), containing abundant sentiments, is crucial for reflecting companies’ sustainable standpoints on its operating ideas, strategies, and methods. For this reason, CSRR can be a valuable source for investors’ decision-making. Fewer researchers have exerted empirical evidence on the sentiment analysis of CSR in China, in the case of information asymmetry
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