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The 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. Introductionĭue to researchers’ unrivalled and explosive expansion in data mining, big data, and artificial intelligence, natural language processing (NLP) in handling bunches of textual data becomes an explosive and prevalent field with great future prospects. 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. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. 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 CSR themes mainly focus on business ethical responsibility, particularly ethical activities. 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. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. 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. 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.