Prediction of Happy Endings in German Novels based on Sentiment Information

Prediction of Happy Endings in German Novels

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%.

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...

Towards Sentiment Analysis on German Literature

Sentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentencelevel Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach...

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...

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