Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
Copied to clipboard
| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
Similar Papers
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
Copied to clipboard
| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs (C18-1)
Copied to clipboard
| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
| Approach: | They propose to combine convolutional and fully connected layers in a non-sequential manner to train deep multi-task learning models trained for all emotions at once in unified architecture. |
| Outcome: | The proposed model outperforms the previous system by 0.044 or 4.4% on the WASSA’17 EmoInt shared task dataset. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
Copied to clipboard
| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
Modeling Protagonist Emotions for Emotion-Aware Storytelling (2020.emnlp-main)
Copied to clipboard
| Challenge: | Cognitive scientists have pinpointed the central role of emotions in storytelling. |
| Approach: | They propose to use Emotion Supervision and two Emotion-Reinforced models to generate stories that follow the desired emotion arcs for the protagonist. |
| Outcome: | The proposed models generate stories that follow the desired emotion arcs without sacrificing story quality. |
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that user-level features can carry more task-related information than the text itself. |
| Approach: | They evaluate multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users’ self-expression and psychological attributes. |
| Outcome: | The proposed models improve on the language and traits of users, while lacking rich annotations of other attributes. |
Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation (2020.lrec-1)
Copied to clipboard
Piroska Lendvai, Sándor Darányi, Christian Geng, Moniek Kuijpers, Oier Lopez de Lacalle, Jean-Christophe Mensonides, Simone Rebora, Uwe Reichel
| Challenge: | a new study aims to detect how and when readers are experiencing engagement with a literary work . empirical literary studies and language technology are used to investigate reading absorption . |
| Approach: | They annotated user-generated book reviews with reading absorption categories . they then performed supervised binary classification of the mental state of absorption . |
| Outcome: | The proposed corpus of user-generated reviews is compared with machine learning models and a benchmark corpus. |
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading. |
| Approach: | They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets. |
| Outcome: | The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans. |
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
Measuring Information Propagation in Literary Social Networks (2020.emnlp-main)
Copied to clipboard
| Challenge: | a gap in computational work to support the "Miss Havisham is dead" "She died" research focuses on the representation of social networks in literature . |
| Approach: | They propose a pipeline for measuring information propagation in literature . they analyze the dynamics of information propagations in over 5,000 works of fiction . |
| Outcome: | The proposed pipeline analyzes the dynamics of information propagation in 5,000 works of fiction and finds that women fill structural holes connecting different communities more frequently than men. |