Publications

2021

The FairyNet Corpus - Character Networks for German Fairy Tales. Schmidt, David; Zehe, Albin; Lorenzen, Janne; Sergel, Lisa; Düker, Sebastian; Krug, Markus; Puppe, Frank (2021). 49–56.
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This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.
@inproceedings{schmidt2021fairynet, abstract = {This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.}, address = {Punta Cana, Dominican Republic (online)}, author = {Schmidt, David and Zehe, Albin and Lorenzen, Janne and Sergel, Lisa and D{ü}ker, Sebastian and Krug, Markus and Puppe, Frank}, booktitle = {Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature}, keywords = {characters dataset kallimachos myown networks nlp}, month = {nov}, pages = {49–56}, publisher = {Association for Computational Linguistics}, title = {The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales}, year = 2021 }

Assessing Media Bias in Cross-Linguistic and Cross-National Populations. Sales, Allan; Zehe, Albin; Marinho, Leandro Balby; Veloso, Adriano; Hotho, Andreas; Omeliyanenko, Janna in Proceedings of the International AAAI Conference on Web and Social Media (2021). 15(1) 561–572.
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@article{sales2021assessing, author = {Sales, Allan and Zehe, Albin and Marinho, Leandro Balby and Veloso, Adriano and Hotho, Andreas and Omeliyanenko, Janna}, journal = {Proceedings of the International AAAI Conference on Web and Social Media}, keywords = {bias icwsm media myown sentiment}, month = {may}, number = 1, pages = {561-572}, title = {Assessing Media Bias in Cross-Linguistic and Cross-National Populations}, volume = 15, year = 2021 }

Detecting Scenes in Fiction: A new Segmentation Task. Zehe, Albin; Konle, Leonard; Dümpelmann, Lea; Gius, Evelyn; Hotho, Andreas; Jannidis, Fotis; Kaufmann, Lucas; Krug, Markus; Puppe, Frank; Reiter, Nils; Schreiber, Annekea; Wiedmer, Nathalie (2021).
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@inproceedings{zehe2021detecting, author = {Zehe, Albin and Konle, Leonard and Dümpelmann, Lea and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea and Wiedmer, Nathalie}, booktitle = {Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, keywords = {dataset dfg-antrag-steckbriefe eacl kallimachos myown myown:selected scenedetection scenes task}, publisher = {ACL}, title = {Detecting Scenes in Fiction: A new Segmentation Task}, year = 2021 }

Shared Task on Scene Segmentation @ KONVENS 2021. Zehe, Albin; Konle, Leonard; Guhr, Svenja; Dümpelmann, Lea; Gius, Evelyn; Hotho, Andreas; Jannidis, Fotis; Kaufmann, Lucas; Krug, Markus; Puppe, Frank; Reiter, Nils; Schreiber, Annekea (2021). 1–21.
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@inproceedings{zehe2021shared, author = {Zehe, Albin and Konle, Leonard and Guhr, Svenja and Dümpelmann, Lea and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea}, booktitle = {Shared Task on Scene Segmentation @ KONVENS 2021}, keywords = {cls dh myown nlp scenedetection scenes stss}, pages = {1-21}, title = {Shared Task on Scene Segmentation @ KONVENS 2021}, year = 2021 }

2020

MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images. Steininger, Michael; Kobs, Konstantin; Zehe, Albin; Lautenschlager, Florian; Becker, Martin; Hotho, Andreas in ACM Trans. Spatial Algorithms Syst. (2020). 6(3)
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Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.
@article{10.1145/3380973, abstract = {Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.}, address = {New York, NY, USA}, author = {Steininger, Michael and Kobs, Konstantin and Zehe, Albin and Lautenschlager, Florian and Becker, Martin and Hotho, Andreas}, journal = {ACM Trans. Spatial Algorithms Syst.}, keywords = {Land-use air deep learning myown pollution regression}, month = {apr}, number = 3, publisher = {Association for Computing Machinery}, title = {MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images}, volume = 6, year = 2020 }

SimLoss: Class Similarities in Cross Entropy. Kobs, Konstantin; Steininger, Michael; Zehe, Albin; Lautenschlager, Florian; Hotho, Andreas (2020).
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One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https://github.com/konstantinkobs/SimLoss.
@misc{kobs2020simloss, abstract = {One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https://github.com/konstantinkobs/SimLoss.}, author = {Kobs, Konstantin and Steininger, Michael and Zehe, Albin and Lautenschlager, Florian and Hotho, Andreas}, keywords = {crossentropy lossfunction myown neuralnet}, note = {cite arxiv:2003.03182Comment: This paper is going to be published in the proceedings of the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS)}, title = {SimLoss: Class Similarities in Cross Entropy}, year = 2020 }

Time Series Forecasting for Self-Aware Systems. Bauer, A.; Züfle, M.; Herbst, N.; Zehe, A.; Hotho, A.; Kounev, S. in Proceedings of the IEEE (2020). 1–26.
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Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.
@article{9069193, abstract = {Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.}, author = {{Bauer}, A. and {Züfle}, M. and {Herbst}, N. and {Zehe}, A. and {Hotho}, A. and {Kounev}, S.}, journal = {Proceedings of the IEEE}, keywords = {Forecasting myown myown:selected timeseries}, pages = {1-26}, title = {Time Series Forecasting for Self-Aware Systems}, year = 2020 }

Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels. Kobs, Konstantin; Zehe, Albin; Bernstetter, Armin; Chibane, Julian; Pfister, Jan; Tritscher, Julian; Hotho, Andreas in ACM Transactions on Social Computing (2020).
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@article{noauthororeditor, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {ACM Transactions on Social Computing}, keywords = {emote myown myown:selected nlp semantic sentiment sentiment-analysis sentimentanalysis twitch}, note = {accepted but not published}, title = {Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels}, year = 2020 }

HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis. Zehe, Albin; Arns, Julia; Hettinger, Lena; Hotho, Andreas (2020).
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@inproceedings{zehe2020harrymotions, author = {Zehe, Albin and Arns, Julia and Hettinger, Lena and Hotho, Andreas}, booktitle = {5th SwissText & 16th KONVENS Joint Conference}, keywords = {bert dfg-antrag-steckbriefe harrymotions harrypotter myown myown:selected nlp proposal-knowledge sentiment sentimentanalysis}, title = {HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis}, year = 2020 }

Towards Predicting the Subscription Status of Twitch.tv Users. Kobs, Konstantin; Potthast, Martin; Wiegmann, Matti; Zehe, Albin; Stein, Benno; Hotho, Andreas in Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch (2020).
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@article{kobstowards, author = {Kobs, Konstantin and Potthast, Martin and Wiegmann, Matti and Zehe, Albin and Stein, Benno and Hotho, Andreas}, journal = {Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch}, keywords = {myown twitch}, title = {Towards Predicting the Subscription Status of Twitch.tv Users}, year = 2020 }

Where to Submit? Helping Researchers to Choose the Right Venue. Kobs, Konstantin; Koopmann, Tobias; Zehe, Albin; Fernes, David; Krop, Philipp; Hotho, Andreas (2020). 878–883.
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@inproceedings{kobs-etal-2020-submit, address = {Online}, author = {Kobs, Konstantin and Koopmann, Tobias and Zehe, Albin and Fernes, David and Krop, Philipp and Hotho, Andreas}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings}, keywords = {dfg-antrag-steckbriefe myown wts}, month = {nov}, pages = {878–883}, publisher = {Association for Computational Linguistics}, title = {Where to Submit? Helping Researchers to Choose the Right Venue}, year = 2020 }

LM4KG: Improving Common Sense Knowledge Graphs with Language Models. Omeliyanenko, Janna; Zehe, Albin; Hettinger, Lena; Hotho, Andreas (2020).
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@inproceedings{omeliyanenko2020reweight, author = {Omeliyanenko, Janna and Zehe, Albin and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Semantic Web Conference}, keywords = {dfg-antrag-steckbriefe graph kg knowledge language lm models myown nlp}, organization = {Springer}, title = {LM4KG: Improving Common Sense Knowledge Graphs with Language Models}, year = 2020 }

Improving Sentiment Analysis with Biofeedback Data. Schlör, Daniel; Zehe, Albin; Kobs, Konstantin; Veseli, Blerta; Westermeier, Franziska; Brübach, Larissa; Roth, Daniel; Latoschik, Marc Erich; Hotho, Andreas (2020). 28–33.
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Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.
@inproceedings{schlor-etal-2020-improving, abstract = {Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.}, address = {Marseille, France}, author = {Schl{ö}r, Daniel and Zehe, Albin and Kobs, Konstantin and Veseli, Blerta and Westermeier, Franziska and Br{ü}bach, Larissa and Roth, Daniel and Latoschik, Marc Erich and Hotho, Andreas}, booktitle = {Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)}, keywords = {biofeedback mutlimodal myown neuralnet sentiment sentimentanalysis}, month = {may}, pages = {28–33}, publisher = {European Language Resources Association (ELRA)}, title = {Improving Sentiment Analysis with Biofeedback Data}, year = 2020 }

2019

EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification. Hettinger, Lena; Zehe, Albin; Dallmann, Alexander; Hotho, Andreas K. David, K. Geihs, M. Lange, G. Stumme (eds.) (2019). 191–204.
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@inproceedings{mci/Hettinger2019, address = {Bonn}, author = {Hettinger, Lena and Zehe, Albin and Dallmann, Alexander and Hotho, Andreas}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, editor = {David, Klaus and Geihs, Kurt and Lange, Martin and Stumme, Gerd}, keywords = {claire dfg-antrag-steckbriefe myown relationclassification relations}, pages = {191-204}, publisher = {Gesellschaft für Informatik e.V.}, title = {EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification}, year = 2019 }

Detection of Scenes in Fiction. Gius, Evelyn; Jannidis, Fotis; Krug, Markus; Zehe, Albin; Hotho, Andreas; Puppe, Frank; Krebs, Jonathan; Reiter, Nils; Wiedmer, Nathalie; Konle, Leonard (2019).
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@inproceedings{gius2019detection, author = {Gius, Evelyn and Jannidis, Fotis and Krug, Markus and Zehe, Albin and Hotho, Andreas and Puppe, Frank and Krebs, Jonathan and Reiter, Nils and Wiedmer, Nathalie and Konle, Leonard}, booktitle = {Proceedings of Digital Humanities 2019}, keywords = {dfg-antrag-steckbriefe dh dh2019 kallimachos myown nlp scenedetection scenes}, title = {Detection of Scenes in Fiction}, year = 2019 }

Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks. Zehe, Albin; Hettinger, Lena; Ernst, Stefan; Hauptmann, Christian; Hotho, Andreas (2019).
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@inproceedings{zehe2019xenophilius, author = {Zehe, Albin and Hettinger, Lena and Ernst, Stefan and Hauptmann, Christian and Hotho, Andreas}, booktitle = {Proceedings of The 13th International Workshop on Semantic Evaluation}, keywords = {cnn hyperpartisan myown neuralnets semeval}, publisher = {Association for Computational Linguistics}, title = {Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks}, year = 2019 }

2018

Analysing Direct Speech in German Novels. Jannidis, Fotis; Konle, Leonard; Zehe, Albin; Hotho, Andreas; Krug, Markus (2018).
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@inproceedings{jannidis2018analysing, author = {Jannidis, Fotis and Konle, Leonard and Zehe, Albin and Hotho, Andreas and Krug, Markus}, booktitle = {DHd 2018}, keywords = {directspeech kallimachos myown myown:selected}, title = {Analysing Direct Speech in German Novels}, year = 2018 }

Burrows Zeta: Varianten und Evaluation. Schöch, Christof; Calvo, José; Zehe, Albin; Hotho, Andreas (2018).
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@inproceedings{schoch2018burrows, author = {Schöch, Christof and Calvo, José and Zehe, Albin and Hotho, Andreas}, booktitle = {DHd 2018}, keywords = {cligs myown zeta}, title = {Burrows Zeta: Varianten und Evaluation}, year = 2018 }

Burrows’ Zeta: Exploring and Evaluating Variants and Parameters. Schöch, Christof; Schlör, Daniel; Zehe, Albin; Gebhard, Henning; Becker, Martin; Hotho, Andreas (2018). 274–277.
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@inproceedings{schoech2018zeta, author = {Schöch, Christof and Schlör, Daniel and Zehe, Albin and Gebhard, Henning and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {cligs myown zeta}, pages = {274-277}, title = {Burrows’ Zeta: Exploring and Evaluating Variants and Parameters}, year = 2018 }

ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas (2018).
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@inproceedings{hettinger2018semeval, address = {New Orleans, LA, USA}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)}, keywords = {2018 classification dfg-antrag-steckbriefe embeddings myown relation scientific semeval w2v word}, title = {ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings}, year = 2018 }

A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels. Zehe, Albin; Schlör, Daniel; Henny-Krahmer, Ulrike; Becker, Martin; Hotho, Andreas (2018).
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@inproceedings{zehe2018whitebox, author = {Zehe, Albin and Schlör, Daniel and Henny-Krahmer, Ulrike and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {author classification cligs myown nationality svn}, organization = {ADHO}, title = {A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels}, year = 2018 }

2017

Towards Sentiment Analysis on German Literature. Zehe, Albin; Becker, Martin; Jannidis, Fotis; Hotho, Andreas (2017). 387–394.
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@inproceedings{zehe2017sentiment, author = {Zehe, Albin and Becker, Martin and Jannidis, Fotis and Hotho, Andreas}, booktitle = {Joint German/Austrian Conference on Artificial Intelligence (K{ü}nstliche Intelligenz)}, keywords = {cnn kallimachos ma-zehe myown myown:selected neuralnet sentimentanalysis}, organization = {Springer}, pages = {387-394}, title = {Towards Sentiment Analysis on German Literature}, year = 2017 }

Sentiment Analysis on German Novels Zehe, Albin (2017).
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@mastersthesis{zehe2017sentiment, author = {Zehe, Albin}, keywords = {cnn kallimachos ma-zehe myown neuralnet sentimentanalysis}, title = {Sentiment Analysis on German Novels}, year = 2017 }

2016

Analyzing Features for the Detection of Happy Endings in German Novels. Jannidis, Fotis; Reger, Isabella; Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas (2016).
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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.
@misc{jannidis2016analyzing, 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.}, author = {Jannidis, Fotis and Reger, Isabella and Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas}, keywords = {gpugrant happyend kallimachos myown proposal-knowledge sentimentanalysis}, note = {cite arxiv:1611.09028}, title = {Analyzing Features for the Detection of Happy Endings in German Novels}, year = 2016 }

Prediction of Happy Endings in German Novels. Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas; Reger, Isabella; Jannidis, Fotis P. Cellier, T. Charnois, A. Hotho, S. Matwin, M.-F. Moens, Y. Toussaint (eds.) (2016). 9–16.
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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 SVM- classifier, which yields an average F1-score of about 73%.
@inproceedings{zehe2016prediction, abstract = {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 SVM- classifier, which yields an average F1-score of about 73%.}, author = {Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis}, booktitle = {Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, keywords = {gpugrant happyendecml kallimachos ma-zehe myown myown:selected sentimentanalysis}, month = {jul}, pages = {9-16}, title = {Prediction of Happy Endings in German Novels}, year = 2016 }