Prediction of Happy Endings in German Novels

Prediction of Happy Endings in German Novels based on Sentiment Information

Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVMclassifier, which yields an average F1-score of about 73%.

The Rewards of Impertinence: Happy and Unhappy Endings in Jane Austen's Novels

بررسی ویژگی محتوایی و شخصیت‌پردازی رمان‌های پرفروش نوجوان منتشر شده بین سال‌های 1389-1380

Purpose: To review the content and the characters of the parsonage in Persian youth best–selling novels between the 2000-2010s. Methodology: Quantitative content analysis was carried out on 14 novels for the youth reprinted more than ten times. Titles were chosen from the list prepared by the Book House. Findings: Most characters faced similar problems, such as poverty, physical illness or me...

Analyzing Features for the Detection of Happy Endings in German Novels

With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we stu...

A Nonlinear Model of Economic Data Related to the German Automobile Industry

Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. But it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. Such evidence appeared in the German automobile industry as a consequence of the financial crisis in 2008/09, which influenced exchange rates and a...