Analysing the Textual Analysis of Different NLP Techniques to Classify the Stability of Corporate Reporting

Presently, in certain cases, unstructured material like annual reports, news articles, and earnings call transcripts may give valuable sustainability data. Currently, scholars and specialists have started the collection of data from many sources employing a wide array of natural language processing (NLP) techniques. Although nearby many benefits to be obtained from these efforts, studies that use these techniques often fail to consider the accuracy and effectiveness of the selected approach in capturing sustainability information from text. This method is troublesome due to the variability in outcomes that arise from using multiple NLP algorithms for information extraction. Therefore, the selection of a particular approach might have an impact on the output of an application and subsequently influence the conclusions that users get from their findings. This research investigates the impact of several NLP techniques on the accuracy and excellence of retrieved information. The researcher specifically analyses and contrasts four main methods.