Analyzing Features for the Detection of Happy Endings in German Novels
📝 Abstract
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 study various partitionings of novels, considering the highly variable concept of “ending”. We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.
💡 Analysis
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 study various partitionings of novels, considering the highly variable concept of “ending”. We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.
📄 Content
Analyzing Features for the Detection of Happy Endings in German Novels Fotis Jannidis, Isabella Reger, Albin Zehe, Martin Becker, Lena Hettinger, Andreas Hotho
Abstract 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 study various partitionings of novels, considering the highly variable concept of “ending”. We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.
Introduction Plot is fundamental for the structure of literary works. Methods for the computational
representation of plot or special plot elements would therefore be a great achievement for
digital literary studies. This paper looks at one such element: happy endings. We employ sentiment analysis for the detection of happy endings, but focus on a qualitative
analysis of specific features and their performance in order to gain deeper insight into the
automatic classification. In addition, we show how the applied method can be used for
subsequent research questions, yielding interesting results with regard to publishing periods
of the novels.
Related Work One of the first works was on folkloristic tales, done by Mark Finlayson, who created an
algorithm capable of detecting events and higher-level abstractions, such as villainy or
reward (Finlayson 2012). Reiter et al., again on tales, identify events, their participants and
order and use machine learning methods to find structural similarities across texts (Reiter
2013, Reiter et al. 2014).
Recently, a significant amount of attention has been paid to sentiment analysis, when
Matthew Jockers proposed emotional arousal as a new “method for detecting plot” (Jockers
2014). He described his idea to split novels into segments and use those to form plot
trajectories (Jockers 2015). Despite general acceptance of the idea to employ sentiment
analysis, his use of the Fourier Transformation to smooth the resulting plot curves was
criticized (Swafford 2015, Schmidt 2015). Among other features, Micha Elsner (Elsner 2015) builds plot representations of romantic
novels, again by using sentiment trajectories. He also links such trajectories with specific
characters and looks at character co-occurrences. To evaluate his approach, he
distinguishes real novels from artificially reordered surrogates with considerable success,
showing that his methods indeed capture certain aspects of plot structure. In previous work, we used sentiment features to detect happy endings as a major plot
element in German novels, reaching an F1-score of 73% (Zehe et al. 2016).
Corpus and Resources Our dataset consists of 212 novels in German language mostly from the 19th century . Each
1 novel has been manually annotated as either having a happy ending (50%) or not (50%).
The relevant information has been obtained from summaries of the Kindler Literary Lexikon
Online and Wikipedia. If no summary was available, the corresponding parts of the novel
2 have been read by the annotators.
Sentiment analysis requires a resource which lists sentiment values that human readers
typically associate with certain words or phrases in a text. This paper relies on the NRC
Sentiment Lexicon (Mohammad and Turney 2013), which is available in an automatically
translated German version . A notable feature of this lexicon is that besides specifying binary
3 values (0 or 1) for negative and positive connotations (2 features) it also categorizes words
into 8 basic emotions (anger, fear, disgust, surprise, joy, anticipation, trust and sadness),
see Table 1 for an example. We add another value (the polarity) by subtracting the negative
from the positive value (e.g. a word with a positive value of 0 and a negative value of 1 has a
polarity value of -1). The pol
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