Sven Buechel

Also published as: Sven Büchel


2021

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Towards Label-Agnostic Emotion Embeddings
Sven Buechel | Luise Modersohn | Udo Hahn
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate. To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality. Code and data are archived under DOI 10.5281 / zenodo.5466068.

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Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Orphee De Clercq | Alexandra Balahur | Joao Sedoc | Valentin Barriere | Shabnam Tafreshi | Sven Buechel | Veronique Hoste
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2019

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Modeling Word Emotion in Historical Language : Quantity Beats Supposed Stability in Seed Word Selection
Johannes Hellrich | Sven Buechel | Udo Hahn
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

To understand historical texts, we must be aware that languageincluding the emotional connotation attached to wordschanges over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it. To overcome this limitation, we present adaptations of two popular algorithms to VAD. To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German. In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harm- rather than helpful.

2018

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Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level
Sven Buechel | Udo Hahn
Proceedings of the 27th International Conference on Computational Linguistics

Emotion Representation Mapping (ERM) has the goal to convert existing emotion ratings from one representation format into another one, e.g., mapping Valence-Arousal-Dominance annotations for words or sentences into Ekman’s Basic Emotions and vice versa. ERM can thus not only be considered as an alternative to Word Emotion Induction (WEI) techniques for automatic emotion lexicon construction but may also help mitigate problems that come from the proliferation of emotion representation formats in recent years. We propose a new neural network approach to ERM that not only outperforms the previous state-of-the-art. Equally important, we present a refined evaluation methodology and gather strong evidence that our model yields results which are (almost) as reliable as human annotations, even in cross-lingual settings. Based on these results we generate new emotion ratings for 13 typologically diverse languages and claim that they have near-gold quality, at least.

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JeSemE : Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change PhenomenaJeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena
Johannes Hellrich | Sven Buechel | Udo Hahn
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora. JeSemE is designed for scholars in the (digital) humanities as an alternative to consulting manually compiled, printed dictionaries for such information (if available at all). This tool uniquely combines state-of-the-art distributional semantics with a nuanced model of human emotions, two information streams we deem beneficial for a data-driven interpretation of texts in the humanities.

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Modeling Empathy and Distress in Reaction to News Stories
Sven Buechel | Anneke Buffone | Barry Slaff | Lyle Ungar | João Sedoc
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations : It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.

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A Corpus of Corporate Annual and Social Responsibility Reports : 280 Million Tokens of Balanced Organizational Writing
Sebastian G.M. Händschke | Sven Buechel | Jan Goldenstein | Philipp Poschmann | Tinghui Duan | Peter Walgenbach | Udo Hahn
Proceedings of the First Workshop on Economics and Natural Language Processing

We introduce JOCo, a novel text corpus for NLP analytics in the field of economics, business and management. This corpus is composed of corporate annual and social responsibility reports of the top 30 US, UK and German companies in the major (DJIA, FTSE 100, DAX), middle-sized (S&P 500, FTSE 250, MDAX) and technology (NASDAQ, FTSE AIM 100, TECDAX) stock indices, respectively. Altogether, this adds up to 5,000 reports from 270 companies headquartered in three of the world’s most important economies. The corpus spans a time frame from 2000 up to 2015 and contains, in total, 282 M tokens. We also feature JOCo in a small-scale experiment to demonstrate its potential for NLP-fueled studies in economics, business and management research.

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A Method for Human-Interpretable Paraphrasticality Prediction
Maria Moritz | Johannes Hellrich | Sven Büchel
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

The detection of reused text is important in a wide range of disciplines. However, even as research in the field of plagiarism detection is constantly improving, heavily modified or paraphrased text is still challenging for current methodologies. For historical texts, these problems are even more severe, since text sources were often subject to stronger and more frequent modifications. Despite the need for tools to automate text criticism, e.g., tracing modifications in historical text, algorithmic support is still limited. While current techniques can tell if and how frequently a text has been modified, very little work has been done on determining the degree and kind of paraphrastic modificationdespite such information being of substantial interest to scholars. We present a human-interpretable, feature-based method to measure paraphrastic modification. Evaluating our technique on three data sets, we find that our approach performs competitive to text similarity scores borrowed from machine translation evaluation, being much harder to interpret.

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Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem
Sven Buechel | Udo Hahn
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Predicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has been shifted to more expressive emotion representation models (such as Basic Emotions or Valence-Arousal-Dominance). This change resulted in a proliferation of heterogeneous formats and, in parallel, often small-sized, non-interoperable resources (lexicons and corpus annotations). In particular, the limitations in size hampered the application of deep learning methods in this area because they typically require large amounts of input data. We here present a solution to get around this language data bottleneck by rephrasing word emotion induction as a multi-task learning problem. In this approach, the prediction of each independent emotion dimension is considered as an individual task and hidden layers are shared between these dimensions. We investigate whether multi-task learning is more advantageous than single-task learning for emotion prediction by comparing our model against a wide range of alternative emotion and polarity induction methods featuring 9 typologically diverse languages and a total of 15 conditions. Our model turns out to outperform each one of them. Against all odds, the proposed deep learning approach yields the largest gain on the smallest data sets, merely composed of one thousand samples.

2017

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Readers vs. Writers vs. Texts : Coping with Different Perspectives of Text Understanding in Emotion Annotation
Sven Buechel | Udo Hahn
Proceedings of the 11th Linguistic Annotation Workshop

We here examine how different perspectives of understanding written discourse, like the reader’s, the writer’s or the text’s point of view, affect the quality of emotion annotations. We conducted a series of annotation experiments on two corpora, a popular movie review corpus and a genre- and domain-balanced corpus of standard English. We found statistical evidence that the writer’s perspective yields superior annotation quality overall. However, the quality one perspective yields compared to the other(s) seems to depend on the domain the utterance originates from. Our data further suggest that the popular movie review data set suffers from an atypical bimodal distribution which may decrease model performance when used as a training resource.

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EmoBank : Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion AnalysisEmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis
Sven Buechel | Udo Hahn
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer’s and reader’s emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader’s perspective in terms of IAA and rating intensity, and achieve close-to-human performance when mapping between dimensional and categorical formats.