Knowledge discovery from the texts of Nobel Prize winners in literature: sentiment analysis and Latent Dirichlet Allocation

Today, The Nobel Prize for Literature is one of the most recognized and prestigious awards. Examining the texts of the authors who have received this award and revealing the factors that play an important role in the awarding of this award is very important for the author, the reader and interested parties. In this direction, within the framework of the study, firstly identified the most popular works of the authors who received the Nobel Prize in Literature between 1980 and 2021 and created a data set-corpus. Dictionary-based sentiment analysis, a method for classifying sentiments, and Latent Dirichlet Allocation (LDA), a very popular approach in topic modeling, were applied to this dataset. As a result, the findings obtained from both sentiment and LDA analyzes were evaluated together and it was found that the themes with the highest distribution in the popular texts of Nobel Prize winners are also those with the positive emotional pole and "trust" weighted sentiment. This study is an exemplary resource in that it contributes to the understanding of the structure and emotional character of the related works of Nobel Prize-winning authors and enables readers and authors to quickly and functionally examine large groups of texts in terms of theme and content.