{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/emotion-classification/papers/4","list_of":"/task/emotion-classification","task":"Emotion Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":458,"counts":{"archive_papers_tagged":458,"with_a_code_link":117,"where_syntology_ran_a_sample":10,"not_listed_spam_title":0,"listed":458,"listed_where_code_ran":10,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/emotion-classification","prev":"/task/emotion-classification/papers/3","next":"/task/emotion-classification/papers/5","papers":[{"url":null,"slug":"how-have-we-reacted-to-the-covid-19-pandemic","title":"How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian Emotions Through The Lens of Twitter","date":"2020-08-20","arxiv_id":"2008.09035","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-carrier-recognition-from-personal","title":"Emotion Carrier Recognition from Personal Narratives","date":"2020-08-17","arxiv_id":"2008.07481","repositories_listed":0,"syntology":null},{"url":null,"slug":"eigenemo-spectral-utterance-representation","title":"EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification","date":"2020-08-15","arxiv_id":"2008.06665","repositories_listed":0,"syntology":null},{"url":"/paper/shallow-over-deep-neural-networks-a-empirical","slug":"shallow-over-deep-neural-networks-a-empirical","title":"Shallow over Deep Neural Networks: A empirical analysis for human emotion classification using audio data","date":"2020-07-03","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-analysis-of-preprocessing-for","title":"A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/contextualized-emotion-recognition-in","slug":"contextualized-emotion-recognition-in","title":"Contextualized Emotion Recognition in Conversation as Sequence Tagging","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"more-diverse-dialogue-datasets-via-diversity","title":"More Diverse Dialogue Datasets via Diversity-Informed Data Collection","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-neuro-at-semeval-2020-task-8-multi-modal","title":"Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning","date":"2020-05-21","arxiv_id":"2005.10915","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computational-model-implementing","title":"A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text","date":"2020-05-12","arxiv_id":"2005.06059","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-event-comment-social-media-corpus-for","title":"An Event-comment Social Media Corpus for Implicit Emotion Analysis","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"korean-specific-emotion-annotation-procedure","title":"Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"muse-a-multimodal-dataset-of-stressed-emotion","title":"MuSE: a Multimodal Dataset of Stressed Emotion","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-automatic-construction-and-refinement-of","title":"Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"appraisal-theories-for-emotion-classification","title":"Appraisal Theories for Emotion Classification in Text","date":"2020-03-31","arxiv_id":"2003.14155","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecsp-a-new-task-for-emotion-cause-span-pair","title":"ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification","date":"2020-03-07","arxiv_id":"2003.03507","repositories_listed":0,"syntology":null},{"url":null,"slug":"x-vectors-meet-emotions-a-study-on","title":"x-vectors meet emotions: A study on dependencies between emotion and speaker recognition","date":"2020-02-12","arxiv_id":"2002.05039","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adversarial-learning-framework-for","title":"An adversarial learning framework for preserving users' anonymity in face-based emotion recognition","date":"2020-01-16","arxiv_id":"2001.06103","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperparameters-optimization-for-deep","title":"Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction","date":"2020-01-12","arxiv_id":"2001.03855","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-emotion-recognizing-with-multiple","title":"Ensemble emotion recognizing with multiple modal physiological signals","date":"2020-01-01","arxiv_id":"2001.00191","repositories_listed":0,"syntology":null},{"url":null,"slug":"goodnewseveryone-a-corpus-of-news-headlines","title":"GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception","date":"2019-12-06","arxiv_id":"1912.03184","repositories_listed":0,"syntology":null},{"url":null,"slug":"converting-sentiment-annotated-data-to","title":"Converting Sentiment Annotated Data to Emotion Annotated Data","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-phonetic-bases-of-vocal-expressed-emotion","title":"The phonetic bases of vocal expressed emotion: natural versus acted","date":"2019-11-13","arxiv_id":"1911.05733","repositories_listed":0,"syntology":null},{"url":null,"slug":"seq2emo-for-multi-label-emotion","title":"Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation","date":"2019-11-06","arxiv_id":"1911.02147","repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-invariant-affective-representation","title":"Speaker-invariant Affective Representation Learning via Adversarial Training","date":"2019-11-04","arxiv_id":"1911.01533","repositories_listed":0,"syntology":null},{"url":"/paper/improving-multi-label-emotion-classification-1","slug":"improving-multi-label-emotion-classification-1","title":"Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-propaganda-detection-in-news","title":"Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-reference-neural-tts-stylization-with","title":"Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency","date":"2019-10-25","arxiv_id":"1910.11958","repositories_listed":0,"syntology":null},{"url":null,"slug":"objective-human-affective-vocal-expression","title":"Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems","date":"2019-10-04","arxiv_id":"1910.01967","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-learning-to-detect-emotions-and","title":"Jointly Learning to Detect Emotions and Predict Facebook Reactions","date":"2019-09-24","arxiv_id":"1909.10779","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-representations-for-low","title":"Contextualized Representations for Low-resource Utterance Tagging","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-for-confounders-in-multimodal","title":"Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning","date":"2019-08-23","arxiv_id":"1908.08979","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-evaluation-of-supervised-machine","title":"Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content","date":"2019-08-05","arxiv_id":"1908.01587","repositories_listed":0,"syntology":null},{"url":null,"slug":"emobed-strengthening-monomodal-emotion","title":"EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings","date":"2019-07-23","arxiv_id":"1907.10428","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-hsu-adopting-pre-trained-bert-for","title":"EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification","date":"2019-07-23","arxiv_id":"1907.09669","repositories_listed":0,"syntology":null},{"url":null,"slug":"caire_hkust-at-semeval-2019-task-3-1","title":"CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification","date":"2019-06-10","arxiv_id":"1906.04041","repositories_listed":0,"syntology":null},{"url":null,"slug":"brainee-at-semeval-2019-task-3-ensembling","title":"BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"caire_hkust-at-semeval-2019-task-3","title":"CAiRE\\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"podlab-at-semeval-2019-task-3-the-importance","title":"Podlab at SemEval-2019 Task 3: The Importance of Being Shallow","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sinai-at-semeval-2019-task-3-using-affective","title":"SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/symantoresearch-at-semeval-2019-task-3","slug":"symantoresearch-at-semeval-2019-task-3","title":"SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thu_ngn-at-semeval-2019-task-3-dialog-emotion","title":"THU\\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/crowdsourcing-and-validating-event-focused","slug":"crowdsourcing-and-validating-event-focused","title":"Crowdsourcing and Validating Event-focused Emotion Corpora for German and English","date":"2019-05-31","arxiv_id":"1905.13618","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-deep-learning-towards-multi-modal","title":"Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing","date":"2019-05-16","arxiv_id":"1905.07039","repositories_listed":0,"syntology":null},{"url":null,"slug":"190510423","title":"Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals","date":"2019-05-13","arxiv_id":"1905.10423","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-model-for-the-implementation-of","title":"A new model for the implementation of positive and negative emotion recognition","date":"2019-05-01","arxiv_id":"1905.00230","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fine-tuned-embeddings-that-model","title":"Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis","date":"2019-04-05","arxiv_id":"1904.03164","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-adversarial-learning-of-speaker","title":"Towards adversarial learning of speaker-invariant representation for speech emotion recognition","date":"2019-03-22","arxiv_id":"1903.09606","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-emotion-classification","title":"Multimodal Emotion Classification","date":"2019-03-13","arxiv_id":"1903.12520","repositories_listed":0,"syntology":null},{"url":null,"slug":"features-extraction-based-on-an-origami","title":"Features Extraction Based on an Origami Representation of 3D Landmarks","date":"2018-12-12","arxiv_id":"1812.05082","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-emotion-recognition-a-persistent","title":"Towards Emotion Recognition: A Persistent Entropy Application","date":"2018-11-21","arxiv_id":"1811.09607","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-many-moods-of-emotion","title":"The Many Moods of Emotion","date":"2018-10-31","arxiv_id":"1810.13197","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-neural-network-with-topical","title":"An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disney-at-iest-2018-predicting-emotions-using","title":"Disney at IEST 2018: Predicting Emotions using an Ensemble","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hgsgnlp-at-iest-2018-an-ensemble-of-machine","title":"HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multi-label-emotion-classification","title":"Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-learning-for-emotion-classification-and","title":"Joint Learning for Emotion Classification and Emotion Cause Detection","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-writing-systems-change-for-deep","title":"Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nlp-at-iest-2018-bilstm-attention-and-lstm","title":"NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sinai-at-iest-2018-neural-encoding-of","title":"SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-analysis-of-the-role-of","title":"An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs","date":"2018-08-31","arxiv_id":"1808.10653","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-ensemble-framework-for-emotion","title":"A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction","date":"2018-08-03","arxiv_id":"1808.01216","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-annotated-corpora-for-emotion","title":"An Analysis of Annotated Corpora for Emotion Classification in Text","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-emotion-enriched-word","title":"Learning Emotion-enriched Word Representations","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-ar-cnn-dcnn-autoencoder-based","title":"EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-area66-predicting-emotions-in","title":"EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-dlc-self-attentive-bilstm-for","title":"EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-jtml-detecting-emotions-with","title":"EmotionX-JTML: Detecting emotions with Attention","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-dlc-self-attentive-bilstm-for-1","title":"EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue","date":"2018-06-19","arxiv_id":"1806.07039","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-relational-tensor-network-for","title":"Multimodal Relational Tensor Network for Sentiment and Emotion Classification","date":"2018-06-07","arxiv_id":"1806.02923","repositories_listed":0,"syntology":null},{"url":null,"slug":"affecthor-at-semeval-2018-task-1-a-cross","title":"AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amrita_student-at-semeval-2018-task-1","title":"Amrita\\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"crystalfeel-at-semeval-2018-task-1","title":"CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dl-team-at-semeval-2018-task-1-tweet-affect","title":"DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2018-task-1-emotion-intensity","title":"ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"elirf-upv-at-semeval-2018-tasks-1-and-3","title":"ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ema-at-semeval-2018-task-1-emotion-mining-for","title":"EMA at SemEval-2018 Task 1: Emotion Mining for Arabic","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emowordnet-automatic-expansion-of-emotion","title":"EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"foi-dss-at-semeval-2018-task-1-combining-lstm","title":"FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"isclab-at-semeval-2018-task-1-uir-miner-for","title":"ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ku-mtl-at-semeval-2018-task-1-multi-task","title":"KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lt3-at-semeval-2018-task-1-a-classifier-chain","title":"LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mutux-at-semeval-2018-task-1-exploring","title":"Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"plusemo2vec-at-semeval-2018-task-1-exploiting-1","title":"PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \\#hashtags","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"psyml-at-semeval-2018-task-1-transfer","title":"psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2018-task-1-affect-in-tweets","title":"SemEval-2018 Task 1: Affect in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sinai-at-semeval-2018-task-1-emotion","title":"SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tw-star-at-semeval-2018-task-1-preprocessing","title":"Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-cascade-attention-based-rnn-for","title":"Context-aware Cascade Attention-based RNN for Video Emotion Recognition","date":"2018-05-30","arxiv_id":"1805.12098","repositories_listed":0,"syntology":null},{"url":"/paper/modeling-naive-psychology-of-characters-in","slug":"modeling-naive-psychology-of-characters-in","title":"Modeling Naive Psychology of Characters in Simple Commonsense Stories","date":"2018-05-16","arxiv_id":"1805.06533","repositories_listed":0,"syntology":null},{"url":null,"slug":"emtc-multilabel-corpus-in-movie-domain-for","title":"EMTC: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lingmotif-lex-a-wide-coverage-state-of-the","title":"Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-and-clause-level-emotion-annotation","title":"Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-emotions-a-dataset-of-tweets-to","title":"Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"i-know-how-you-feel-emotion-recognition-with","title":"I Know How You Feel: Emotion Recognition with Facial Landmarks","date":"2018-04-22","arxiv_id":"1805.00326","repositories_listed":0,"syntology":null},{"url":null,"slug":"attnconvnet-at-semeval-2018-task-1-attention","title":"AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification","date":"2018-04-03","arxiv_id":"1804.00831","repositories_listed":0,"syntology":null},{"url":null,"slug":"emorl-continuous-acoustic-emotion","title":"EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning","date":"2018-04-03","arxiv_id":"1804.04053","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatically-augmenting-an-emotion-dataset","title":"Automatically augmenting an emotion dataset improves classification using audio","date":"2018-03-30","arxiv_id":"1803.11506","repositories_listed":0,"syntology":null},{"url":"/paper/eeg-emotion-recognition-using-dynamical-graph","slug":"eeg-emotion-recognition-using-dynamical-graph","title":"EEG emotion recognition using dynamical graph convolutional neural networks","date":"2018-03-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-binary-neural-network-for-multi-label","title":"Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification","date":"2018-02-03","arxiv_id":"1802.00891","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-autoencoders-for-learning-latent","title":"Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study","date":"2017-12-23","arxiv_id":"1712.08708","repositories_listed":0,"syntology":null}],"record_sha256":"783117f85977d7253d5d79c3c7be2ac065deb151777b4b1928c763575ce27d99","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}