Table of Contents

• Enhanced Financial Sentiment Analysis and Trading Strategy Development Using Large Language ModelsKemal Kirtac, Guido Germano 0001. 1-10 [doi]
• SEC: Context-Aware Metric Learning for Efficient Emotion Recognition in ConversationBarbara Gendron, GaelGuibon GaelGuibon. 11-22 [doi]
• Modeling Complex Interactions in Long Documents for Aspect-Based Sentiment AnalysisZehong Yan, Wynne Hsu, Mong-Li Lee, David Bartram-Shaw. 23-34 [doi]
• Hierarchical Adversarial Correction to Mitigate Identity Term Bias in Toxicity DetectionJohannes Schäfer, Ulrich Heid, Roman Klinger. 35-51 [doi]
• A Systematic Analysis on the Temporal Generalization of Language Models in Social MediaAsahi Ushio, José Camacho-Collados. 52-62 [doi]
• LLaMA-Based Models for Aspect-Based Sentiment AnalysisJakub Smíd, Pavel Pribán, Pavel Král. 63-70 [doi]
• A Multi-Faceted NLP Analysis of Misinformation Spreaders in TwitterDimosthenis Antypas, Alun D. Preece, José Camacho-Collados. 71-83 [doi]
• Entity-Level Sentiment: More than the Sum of Its PartsEgil Rønningstad, Roman Klinger, Erik Velldal, Lilja Øvrelid. 84-96 [doi]
• MBIAS: Mitigating Bias in Large Language Models While Retaining ContextShaina Raza, Ananya Raval, Veronica Chatrath. 97-111 [doi]
• Polarization of Autonomous Generative AI Agents Under Echo ChambersMasaya Ohagi. 112-124 [doi]
• Know Thine Enemy: Adaptive Attacks on Misinformation Detection Using Reinforcement LearningPiotr Przybyla, Euan McGill, Horacio Saggion. 125-140 [doi]
• The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language ModelsXiliang Zhu, Shayna Gardiner, Tere Roldán, David Rossouw. 141-152 [doi]
• Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicSilvan Wehrli, Chisom Ezekannagha, Georges Hattab, Tamara Boender, Bert Arnrich, Christopher Irrgang. 153-167 [doi]
• Emotion Identification for French in Written Texts: Considering Modes of Emotion Expression as a Step Towards Text Complexity AnalysisAline Étienne, Delphine Battistelli, Gwénolé Lecorvé. 168-185 [doi]
• Comparing Tools for Sentiment Analysis of Danish Literature from Hymns to Fairy Tales: Low-Resource Language and Domain ChallengesPascale Feldkamp Moreira, Jan Kostkan, Ea Overgaard, Mia Jacobsen, Yuri Bizzoni. 186-199 [doi]
• Multi-Target User Stance Discovery on RedditBenjamin Steel, Derek Ruths. 200-214 [doi]
• Subjectivity Detection in English News using Large Language ModelsMohammad Shokri, Vivek Sharma, Elena Filatova, Shweta Jain 0001, Sarah Ita Levitan. 215-226 [doi]
• Monitoring Depression Severity and Symptoms in User-Generated Content: An Annotation Scheme and GuidelinesFalwah AlHamed, Rebecca Bendayan, Julia Ive, Lucia Specia. 227-233 [doi]
• RideKE: Leveraging Low-resource Twitter User-generated Content for Sentiment and Emotion Detection on Code-switched RHS DatasetNaome A. Etori, Maria L. Gini. 234-249 [doi]
• POLygraph: Polish Fake News DatasetDaniel Dzienisiewicz, Filip Gralinski, Piotr Jablonski, Marek Kubis, Pawel Skórzewski, Piotr Wierzchon. 250-263 [doi]
• Exploring Language Models to Analyze Market Demand Sentiments from NewsTirthankar Dasgupta, Manjira Sinha. 264-272 [doi]
• Impact of Decoding Methods on Human Alignment of Conversational LLMsShaz Furniturewala, Kokil Jaidka, Yashvardhan Sharma. 273-279 [doi]
• Loneliness Episodes: A Japanese Dataset for Loneliness Detection and AnalysisNaoya Fujikawa, Nguyen Toan, Kazuhiro Ito, Shoko Wakamiya, Eiji Aramaki. 280-293 [doi]
• Estimation of Happiness Changes through Longitudinal Analysis of Employees' TextsJunko Hayashi, Kazuhiro Ito, Masae Manabe, Yasushi Watanabe, Masataka Nakayama, Yukiko Uchida, Shoko Wakamiya, Eiji Aramaki. 294-304 [doi]
• Subjectivity Theory vs. Speaker Intuitions: Explaining the Results of a Subjectivity Regressor Trained on Native Speaker JudgementsElena Savinova, Jet Hoek. 305-315 [doi]
• Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?Nikita Soni 0002, Niranjan Balasubramanian, H. Andrew Schwartz, Dirk Hovy. 316-328 [doi]
• LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive DilemmaJana Juros, Laura Majer, Jan Snajder. 329-343 [doi]
• Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit DiscussionsNeha Sharma, Kairit Sirts. 344-361 [doi]
• To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span PredictionKemal Kurniawan, Meladel Mistica, Timothy Baldwin, Jey Han Lau. 362-368 [doi]
• Findings of WASSA 2024 Shared Task on Empathy and Personality Detection in InteractionsSalvatore Giorgi, João Sedoc, Valentin Barrière, Shabnam Tafreshi. 369-379 [doi]
• RU at WASSA 2024 Shared Task: Task-Aligned Prompt for Predicting Empathy and DistressHaein Kong, Seonghyeon Moon. 380-384 [doi]
• Chinchunmei at WASSA 2024 Empathy and Personality Shared Task: Boosting LLM's Prediction with Role-play Augmentation and Contrastive Reasoning CalibrationTian Li, Nicolay Rusnachenko, Huizhi Liang. 385-392 [doi]
• Empathify at WASSA 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a BERT-Based Context-Aware Approach for Empathy DetectionArda Numanoglu, Süleyman Ates, Nihan Kesim Cicekli, Dilek Küçük. 393-398 [doi]
• Zhenmei at WASSA-2024 Empathy and Personality Shared Track 2 Incorporating Pearson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational TurnsLiting Huang, Huizhi Liang. 399-403 [doi]
• Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological IndicatorsShaz Furniturewala, Kokil Jaidka. 404-411 [doi]
• NU at WASSA 2024 Empathy and Personality Shared Task: Enhancing Personality Predictions with Knowledge Graphs; A Graphical Neural Network and LightGBM Ensemble ApproachEmmanuel Osei-Brefo, Huizhi Liang. 412-419 [doi]
• Daisy at WASSA 2024 Empathy and Personality Shared Task: A Quick Exploration on Emotional Pattern of Empathy and DistressRendi Chevi, Alham Fikri Aji. 420-424 [doi]
• WASSA 2024 Shared Task: Enhancing Emotional Intelligence with PromptsSvetlana Churina, Preetika Verma, Suchismita Tripathy. 425-429 [doi]
• hyy33 at WASSA 2024 Empathy and Personality Shared Task: Using the CombinedLoss and FGM for Enhancing BERT-based Models in Emotion and Empathy Prediction from Conversation TurnsHuiyu Yang, Liting Huang, Tian Li, Nicolay Rusnachenko, Huizhi Liang. 430-434 [doi]
• Fraunhofer SIT at WASSA 2024 Empathy and Personality Shared Task: Use of Sentiment Transformers and Data Augmentation With Fuzzy Labels to Predict Emotional Reactions in Conversations and EssaysRaphael Antonius Frick, Martin Steinebach. 435-440 [doi]
• EmpatheticFIG at WASSA 2024 Empathy and Personality Shared Task: Predicting Empathy and Emotion in Conversations with Figurative LanguageGyeongeun Lee, Zhu Wang, Sathya N. Ravi, Natalie Parde. 441-447 [doi]
• ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in ConversationsPatrícia Pereira, Helena Moniz, João Paulo Carvalho. 448-453 [doi]
• Findings of the WASSA 2024 EXALT shared task on Explainability for Cross-Lingual Emotion in TweetsAaron Maladry, Pranaydeep Singh, Els Lefever. 454-463 [doi]
• Cross-lingual Emotion Detection through Large Language ModelsRam Mohan Rao Kadiyala. 464-469 [doi]
• Knowledge Distillation from Monolingual to Multilingual Models for Intelligent and Interpretable Multilingual Emotion DetectionYuqi Wang, Zimu Wang, Nijia Han, Wei Wang, Qi Chen, Haiyang Zhang 0004, Yushan Pan, Anh Nguyen 0003. 470-475 [doi]
• HITSZ-HLT at WASSA-2024 Shared Task 2: Language-agnostic Multi-task Learning for Explainability of Cross-lingual Emotion DetectionFeng Xiong, Jun Wang, Geng Tu, Ruifeng Xu 0001. 476-482 [doi]
• UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion DetectionJakub Smíd, Pavel Pribán, Pavel Král. 483-489 [doi]
• PCICUNAM at WASSA 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss FunctionsJesús Vázquez-Osorio, Gerardo Sierra, Helena Gómez-Adorno, Gemma Bel Enguix. 490-494 [doi]
• TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion DetectionLong Cheng, Qihao Shao, Christine Zhao, Sheng Bi, Gina-Anne Levow. 495-504 [doi]
• NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger DetectionTzu-Mi Lin, Zhe-Yu Xu, Jian Yu Zhou, Lung-Hao Lee. 505-510 [doi]
• Effectiveness of Scalable Monolingual Data and Trigger Words Prompting on Cross-Lingual Emotion Detection TaskYao-Fei Cheng, Jeongyeob Hong, Andrew Wang, Anita Silva, Gina-Anne Levow. 511-522 [doi]
• WU_TLAXE at WASSA 2024 Explainability for Cross-Lingual Emotion in Tweets Shared Task 1: Emotion through Translation using TwHIN-BERT and GPTJon Davenport, Keren Ruditsky, Anna Batra, Yulha Lhawa, Gina-Anne Levow. 523-527 [doi]
• Enhancing Cross-Lingual Emotion Detection with Data Augmentation and Token-Label MappingJinghui Zhang, Yuan Zhao, Siqin Zhang, Ruijing Zhao, Siyu Bao. 528-533 [doi]