Table of Contents

• PESTO: A Post-User Fusion Network for Rumour Detection on Social MediaErxue Min, Sophia Ananiadou. 1-10 [doi]
• Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric FeaturesYuri Bizzoni, Pascale Moreira, Mads Rosendahl Thomsen, Kristoffer L. Nielbo. 11-18 [doi]
• Instruction Tuning for Few-Shot Aspect-Based Sentiment AnalysisSiddharth Varia, Shuai Wang, Kishaloy Halder, Robert Vacareanu, Miguel Ballesteros, Yassine Benajiba, Neha Anna John, Rishita Anubhai, Smaranda Muresan, Dan Roth. 19-27 [doi]
• You Are What You Read: Inferring Personality From Consumed Textual ContentAdam Sutton, Almog Simchon, Matthew Edwards, Stephan Lewandowsky. 28-38 [doi]
• UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception DetectionAswathy Velutharambath, Roman Klinger. 39-51 [doi]
• Discourse Mode Categorization of Bengali Social Media Health TextSalim Sazzed. 52-57 [doi]
• Emotion and Sentiment Guided ParaphrasingJustin J. Xie, Ameeta Agrawal. 58-70 [doi]
• Emotions in Spoken Language - Do we need acoustics?Nadine Probol, Margot Mieskes. 71-84 [doi]
• Understanding Emotion Valence is a Joint Deep Learning TaskGabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi. 85-95 [doi]
• Czech-ing the News: Article Trustworthiness Dataset for CzechMatyas Bohacek, Michal Bravansky, Filip Trhlík, Václav Moravec. 96-109 [doi]
• Towards Detecting Harmful Agendas in News ArticlesMelanie Subbiah, Amrita Bhattacharjee, Bobby Yilun Hua, Tharindu Kumarage, Huan Liu 0001, Kathleen R. McKeown. 110-128 [doi]
• GSAC: A Gujarati Sentiment Analysis Corpus from TwitterMonil Gokani, Radhika Mamidi. 129-137 [doi]
• A Dataset for Explainable Sentiment Analysis in the German Automotive IndustryAndrea Zielinski, Calvin Spolwind, Henning Kroll, Anna Grimm. 138-148 [doi]
• Examining Bias in Opinion Summarisation through the Perspective of Opinion DiversityNannan Huang, Lin Tian, Haytham Fayek, Xiuzhen Zhang. 149-161 [doi]
• Fluency Matters! Controllable Style Transfer with Syntax GuidanceJi-Eun Han, Kyung-Ah Sohn. 162-171 [doi]
• ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and LimitationsHamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman. 172-183 [doi]
• Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter DataAkshat Gupta, Xiaomo Liu, Sameena Shah. 184-193 [doi]
• Transformer-based cynical expression detection in a corpus of Spanish YouTube reviewsSamuel González López, Steven Bethard. 194-201 [doi]
• Multilingual Language Models are not Multicultural: A Case Study in EmotionShreya Havaldar, Bhumika Singhal, Sunny Rai, Langchen Liu, Sharath Chandra Guntuku, Lyle H. Ungar. 202-214 [doi]
• Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer ReviewsYukyung Lee, Jaehee Kim, Doyoon Kim, Yookyung Kho, Younsun Kim, Pilsung Kang 0001. 215-227 [doi]
• Context-Dependent Embedding Utterance Representations for Emotion Recognition in ConversationsPatrícia Pereira, Helena Moniz, Isabel Dias, João Paulo Carvalho. 228-236 [doi]
• Combining Active Learning and Task Adaptation with BERT for Cost-Effective Annotation of Social Media DatasetsJens Lemmens, Walter Daelemans. 237-250 [doi]
• Improving Dutch Vaccine Hesitancy Monitoring via Multi-Label Data Augmentation with GPT-3.5Jens Van Nooten, Walter Daelemans. 251-270 [doi]
• Emotion Analysis of Tweets Banning Education in AfghanistanMohammad Ali Hussiny, Lilja Øvrelid. 271-277 [doi]
• Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding SpaceSanne Hoeken, Sina Zarrieß, Özge Alaçam. 278-289 [doi]
• Sentiment and Emotion Classification in Low-resource SettingsJeremy Barnes. 290-304 [doi]
• Analyzing Subjectivity Using a Transformer-Based Regressor Trained on Naïve Speakers' JudgementsElena Savinova, Fermín Moscoso del Prado. 305-314 [doi]
• A Fine Line Between Irony and Sincerity: Identifying Bias in Transformer Models for Irony DetectionAaron Maladry, Els Lefever, Cynthia Van Hee, Véronique Hoste. 315-324 [doi]
• ChatGPT is fun, but it is not funny! Humor is still challenging Large Language ModelsSophie F. Jentzsch, Kristian Kersting. 325-340 [doi]
• How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control TechniquesMichela Lorandi, Anya Belz. 341-353 [doi]
• Transformer-based Prediction of Emotional Reactions to Online Social Network PostsIrene Benedetto, Moreno La Quatra, Luca Cagliero, Luca Vassio, Martino Trevisan. 354-364 [doi]
• Transfer Learning for Code-Mixed Data: Do Pretraining Languages Matter?Kushal Tatariya, Heather C. Lent, Miryam de Lhoneux. 365-378 [doi]
• Can ChatGPT Understand Causal Language in Science Claims?Yuheun Kim, Lu Guo, Bei Yu 0002, Yingya Li. 379-389 [doi]
• Systematic Evaluation of GPT-3 for Zero-Shot Personality EstimationAdithya V. Ganesan, Yash Kumar Lal, August Håkan Nilsson, H. Andrew Schwartz. 390-400 [doi]
• Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across AgeDaniela Teodorescu, Alona Fyshe, Saif M. Mohammad. 401-415 [doi]
• Annotating and Training for Population Subjective ViewsMaria Alexeeva, Caroline Hyland, Keith Alcock, Allegra Argent Beal Cohen, Hubert Kanyamahanga, Isaac Kobby Anni, Mihai Surdeanu. 416-430 [doi]
• Exploration of Contrastive Learning Strategies toward more Robust Stance DetectionUdhaya Kumar Rajendran, Amine Trabelsi. 431-440 [doi]
• Adapting Emotion Detection to Analyze Influence Campaigns on Social MediaAnkita Bhaumik, Andy Bernhardt, Gregorios A. Katsios, Ning Sa, Tomek Strzalkowski. 441-451 [doi]
• Not Just Iconic: Emoji Interpretation is Shaped by UseBrianna O'Boyle, Gabriel Doyle. 452-457 [doi]
• The Paradox of Multilingual Emotion DetectionLuna De Bruyne. 458-466 [doi]
• Sadness and Anxiety Language in Reddit Messages Before and After Quitting a JobMolly Ireland, Micah Iserman, Kiki Adams. 467-478 [doi]
• Communicating Climate Change: A Comparison Between Tweets and Speeches by German Members of ParliamentRobin Schaefer, Christoph Abels, Stephan Lewandowsky, Manfred Stede. 479-496 [doi]
• Modelling Political Aggression on Social Media PlatformsAkash Rawat, Nazia Nafis, Dnyaneshwar Bhadane, Diptesh Kanojia, V. Rudra Murthy. 497-510 [doi]
• Findings of WASSA 2023 Shared Task on Empathy, Emotion and Personality Detection in Conversation and Reactions to News ArticlesValentin Barrière, João Sedoc, Shabnam Tafreshi, Salvatore Giorgi. 511-525 [doi]
• YNU-HPCC at WASSA-2023 Shared Task 1: Large-scale Language Model with LoRA Fine-Tuning for Empathy Detection and Emotion ClassificationYukun Wang, Jin Wang, Xuejie Zhang. 526-530 [doi]
• AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced DataAditya Patkar, Suraj Chandrashekhar, Ram Mohan Rao Kadiyala. 531-535 [doi]
• Curtin OCAI at WASSA 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple TransformersMd Rakibul Hasan, Md. Zakir Hossain, Tom Gedeon, Susannah Soon, Shafin Rahman. 536-541 [doi]
• Team_Hawk at WASSA 2023 Empathy, Emotion, and Personality Shared Task: Multi-tasking Multi-encoder based transformers for Empathy and Emotion Prediction in ConversationsAddepalli Sai Srinivas, Nabarun Barua, Santanu Pal. 542-547 [doi]
• NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa TransformersTzu-Mi Lin, Jung-Ying Chang, Lung-Hao Lee. 548-552 [doi]
• Domain Transfer for Empathy, Distress, and Personality PredictionFabio Gruschka, Allison Lahnala, Charles Welch, Lucie Flek. 553-557 [doi]
• Converge at WASSA 2023 Empathy, Emotion and Personality Shared Task: A Transformer-based Approach for Multi-Label Emotion ClassificationAditya Paranjape, Gaurav Kolhatkar, Yash Patwardhan, Omkar Gokhale, Shweta C. Dharmadhikari. 558-563 [doi]
• PICT-CLRL at WASSA 2023 Empathy, Emotion and Personality Shared Task: Empathy and Distress Detection using Ensembles of Transformer ModelsTanmay Chavan, Kshitij Deshpande, Sheetal Sonawane. 564-568 [doi]
• Team Bias Busters at WASSA 2023 Empathy, Emotion and Personality Shared Task: Emotion Detection with Generative Pretrained TransformersAndrew Nedilko, Yi Chu. 569-573 [doi]
• HIT-SCIR at WASSA 2023: Empathy and Emotion Analysis at the Utterance-Level and the Essay-LevelXin Lu, Zhuojun Li, Yanpeng Tong, Yanyan Zhao, Bing Qin 0001. 574-580 [doi]
• VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Using Transformers and Stacked EmbeddingsVivek Kumar 0007, Prayag Tiwari, Sushmita Singh. 581-586 [doi]
• Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text MessagesIqra Ameer, Necva Bölücü, Hua Xu, Ali Al Bataineh. 587-595 [doi]
• Emotion classification on code-mixed text messages via soft prompt tuningJinghui Zhang, Dongming Yang, Siyu Bao, Lina Cao, Shunguo Fan. 596-600 [doi]
• PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed TextBhaskara Hanuma Vedula, Prashant Kodali, Manish Shrivastava 0001, Ponnurangam Kumaraguru. 601-605 [doi]
• BpHigh at WASSA 2023: Using Contrastive Learning to build Sentence Transformer models for Multi-Class Emotion Classification in Code-mixed UrduBhavish Pahwa. 606-610 [doi]
• YNU-HPCC at WASSA 2023: Using Text-Mixed Data Augmentation for Emotion Classification on Code-Mixed Text MessageXuqiao Ran, You Zhang, Jin Wang, Dan Xu, Xuejie Zhang. 611-615 [doi]
• Generative Pretrained Transformers for Emotion Detection in a Code-Switching SettingAndrew Nedilko. 616-620 [doi]