{"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/sentiment-analysis/papers/32","list_of":"/task/sentiment-analysis","task":"Sentiment Analysis","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":32,"pages_in_order":57,"rows_per_page":100,"rows":[3101,3200],"of":5630,"counts":{"archive_papers_tagged":5630,"with_a_code_link":1509,"where_syntology_ran_a_sample":220,"not_listed_spam_title":0,"listed":5630,"listed_where_code_ran":220,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":194,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":194,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/sentiment-analysis","prev":"/task/sentiment-analysis/papers/31","next":"/task/sentiment-analysis/papers/33","papers":[{"url":null,"slug":"nlp-uiowa-at-semeval-2020-task-8-you-re-not","title":"NLP\\_UIOWA at SemEval-2020 Task 8: You're Not the Only One Cursed with Knowledge - Multi Branch Model Memotion Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"only-text-only-image-or-both-predicting","title":"Only text? only image? or both? Predicting sentiment of internet memes","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prhlt-upv-at-semeval-2020-task-8-study-of","title":"PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"public-sentiment-on-governmental-covid-19","title":"Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regrexit-or-not-regrexit-aspect-based","title":"Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-creation-and-evaluation-of-aspect","title":"Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robert-a-romanian-bert-model","title":"RoBERT -- A Romanian BERT Model","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-for-emotional-speech","title":"Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-of-english-punjabi-code","title":"Sentiment Analysis of English-Punjabi Code-Mixed Social Media Content","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiments-in-russian-medical-professional","title":"Sentiments in Russian Medical Professional Discourse during the Covid-19 Pandemic","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sesam-at-semeval-2020-task-8-investigating","title":"SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sis-iiith-at-semeval-2020-task-8-an-overview","title":"SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sukhan-corpus-of-hindi-shayaris-annotated","title":"SUKHAN: Corpus of Hindi Shayaris annotated with Sentiment Polarity Information","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactically-aware-cross-domain-aspect-and","title":"Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-graph-attention-network-for","title":"Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-domain-identification-using","title":"Technical Domain Identification using word2vec and BiLSTM","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ui-at-semeval-2020-task-8-text-image-fusion","title":"UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-aspect-level-sentiment","title":"Unsupervised Aspect-Level Sentiment Controllable Style Transfer","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uor-at-semeval-2020-task-8-gaussian-mixture","title":"UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"urszula-wali-nska-at-semeval-2020-task-8","title":"Urszula Wali\\'nska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"will-go-at-semeval-2020-task-9-an-accurate","title":"Will\\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"xlp-at-semeval-2020-task-9-cross-lingual","title":"XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zyy1510-team-at-semeval-2020-task-9-sentiment","title":"Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-signal-decomposition-of-various-word","title":"Blind signal decomposition of various word embeddings based on join and individual variance explained","date":"2020-11-30","arxiv_id":"2011.14496","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-sentiment-analysis-engine-for","title":"A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media","date":"2020-11-29","arxiv_id":"2011.14280","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-panoramic-survey-of-natural-language","title":"A Panoramic Survey of Natural Language Processing in the Arab World","date":"2020-11-25","arxiv_id":"2011.12631","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-humor-focused-sentiment-analysis","title":"Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture","date":"2020-11-23","arxiv_id":"2011.11773","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-isca-bidirectional-inter-sentence","title":"Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text","date":"2020-11-23","arxiv_id":"2011.11465","repositories_listed":0,"syntology":null},{"url":"/paper/does-bert-understand-sentiment-leveraging","slug":"does-bert-understand-sentiment-leveraging","title":"Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models","date":"2020-11-23","arxiv_id":"2011.11673","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-language-independent-network-to","title":"A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis","date":"2020-11-20","arxiv_id":"2011.10358","repositories_listed":0,"syntology":null},{"url":null,"slug":"palomino-ochoa-at-semeval-2020-task-9-robust","title":"Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification","date":"2020-11-18","arxiv_id":"2011.09448","repositories_listed":0,"syntology":null},{"url":null,"slug":"sigmalaw-absa-dataset-for-aspect-based","title":"SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts","date":"2020-11-12","arxiv_id":"2011.06326","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multimodal-accuracy-through","title":"Improving Multimodal Accuracy Through Modality Pre-training and Attention","date":"2020-11-11","arxiv_id":"2011.06102","repositories_listed":0,"syntology":null},{"url":null,"slug":"rule-based-approach-for-party-based","title":"Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts","date":"2020-11-11","arxiv_id":"2011.05675","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-sentiment-annotations-to-sentiment","title":"From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation","date":"2020-11-05","arxiv_id":"2011.03021","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-with-self","title":"Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks","date":"2020-11-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"daga-data-augmentation-with-a-generation","title":"DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks","date":"2020-11-03","arxiv_id":"2011.01549","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-centric-unsupervised-multi-document","title":"Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles","date":"2020-11-03","arxiv_id":"2011.08072","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-shared-private-representation-model-with","title":"A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structure-enhanced-graph-convolutional","title":"A structure-enhanced graph convolutional network for sentiment analysis","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asad-a-twitter-based-benchmark-arabic","title":"ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset","date":"2020-11-01","arxiv_id":"2011.00578","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolution-over-hierarchical-syntactic-and","title":"Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diversified-multiple-instance-learning-for","title":"Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-target-specific-latent-structures","title":"Inducing Target-Specific Latent Structures for Aspect Sentiment Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-analyses-on-storytelling-for","title":"Intelligent Analyses on Storytelling for Impact Measurement","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"less-is-more-attention-supervision-with","title":"Less is More: Attention Supervision with Counterfactuals for Text Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multilingual-resources-for","title":"Leveraging Multilingual Resources for Language Invariant Sentiment Analysis","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-text-data-achilles-heel-of-bert","title":"Noisy Text Data: Achilles’ Heel of BERT","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-reliability-and-validity-of-detecting","title":"On the Reliability and Validity of Detecting Approval of Political Actors in Tweets","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opinion-transmission-network-for-jointly","title":"Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification","date":"2020-11-01","arxiv_id":"2011.00474","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-of-tweets-using","title":"Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-multi-aspect-modeling-for","title":"Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis","date":"2020-11-01","arxiv_id":"2011.00476","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-feature-and-instance-based-domain","title":"Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-the-past-knowledge-to-improve-sentiment","title":"Using the Past Knowledge to Improve Sentiment Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-approach-to-develop-a-sentiment","title":"Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting","date":"2020-10-31","arxiv_id":"2011.00318","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretation-of-nlp-models-through-input","title":"Interpretation of NLP models through input marginalization","date":"2020-10-27","arxiv_id":"2010.13984","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-angry-are-your-customers-sentiment","title":"How angry are your customers? Sentiment analysis of support tickets that escalate","date":"2020-10-26","arxiv_id":"2010.13684","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-aspect-based-sentiment-analysis","title":"Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation","date":"2020-10-26","arxiv_id":"2010.13389","repositories_listed":0,"syntology":null},{"url":"/paper/introducing-syntactic-structures-into-target","slug":"introducing-syntactic-structures-into-target","title":"Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning","date":"2020-10-26","arxiv_id":"2010.13378","repositories_listed":0,"syntology":null},{"url":null,"slug":"transgender-community-sentiment-analysis-from","title":"Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach","date":"2020-10-25","arxiv_id":"2010.13062","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-element-joint-detection-for-aspect","title":"Multiple-element joint detection for Aspect-Based Sentiment Analysis","date":"2020-10-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lt3-at-semeval-2020-task-9-cross-lingual","title":"LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text","date":"2020-10-21","arxiv_id":"2010.11019","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasi-error-free-text-classification-and","title":"Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set","date":"2020-10-21","arxiv_id":"2010.10801","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert2dnn-bert-distillation-with-massive","title":"BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search","date":"2020-10-20","arxiv_id":"2010.10442","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-evaluation-of-the-echo","title":"Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums","date":"2020-10-19","arxiv_id":"2010.09574","repositories_listed":0,"syntology":null},{"url":null,"slug":"nuig-shubhanker-dravidian-codemix-fire2020","title":"NUIG-Shubhanker@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet","date":"2020-10-15","arxiv_id":"2010.07773","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-word-representations-for-tunisian","title":"Learning Word Representations for Tunisian Sentiment Analysis","date":"2020-10-14","arxiv_id":"2010.06857","repositories_listed":0,"syntology":null},{"url":null,"slug":"temperature-check-theory-and-practice-for-1","title":"Temperature check: theory and practice for training models with softmax-cross-entropy losses","date":"2020-10-14","arxiv_id":"2010.07344","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-document-classification-an-application","title":"Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service","date":"2020-10-13","arxiv_id":"2010.12533","repositories_listed":0,"syntology":null},{"url":null,"slug":"gundapusunil-at-semeval-2020-task-8","title":"gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis","date":"2020-10-09","arxiv_id":"2010.04470","repositories_listed":0,"syntology":null},{"url":null,"slug":"gundapusunil-at-semeval-2020-task-9-syntactic","title":"gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data","date":"2020-10-09","arxiv_id":"2010.04395","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sentiment-analysis-over-non-english","title":"Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation","date":"2020-10-07","arxiv_id":"2010.03486","repositories_listed":0,"syntology":null},{"url":null,"slug":"cat-gen-improving-robustness-in-nlp-models","title":"CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation","date":"2020-10-05","arxiv_id":"2010.02338","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-for-reinforcement-learning","title":"Sentiment Analysis for Reinforcement Learning","date":"2020-10-05","arxiv_id":"2010.02316","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-for-roman-urdu-text-over","title":"Sentiment Analysis for Roman Urdu Text over Social Media, a Comparative Study","date":"2020-10-05","arxiv_id":"2010.16408","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-in-education","title":"Aspect-Based Sentiment Analysis in Education Domain","date":"2020-10-03","arxiv_id":"2010.01429","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-sentiment-analysis-and-opinion-mining","title":"Legal Sentiment Analysis and Opinion Mining (LSAOM): Assimilating Advances in Autonomous AI Legal Reasoning","date":"2020-10-02","arxiv_id":"2010.02726","repositories_listed":0,"syntology":null},{"url":null,"slug":"textdecepter-hard-label-black-box-attack-on-1","title":"TextDecepter: Hard Label Black Box Attack on Text Classification","date":"2020-10-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-on-indonesias","title":"Aspect-based Sentiment Analysis on Indonesia’s Tourism Destinations Based on Google Maps User Code-Mixed Reviews (Study Case: Borobudur and Prambanan Temples)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"better-queries-for-aspect-category-sentiment","title":"Better Queries for Aspect-Category Sentiment Classification","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-nostalgic-music-through-lda","title":"Classification of Nostalgic Music Through LDA Topic Modeling and Sentiment Analysis of YouTube Comments in Japanese Songs","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"duo-mu-biao-qing-gan-fen-lei-zhong-wen-shu-ju","title":"多目标情感分类中文数据集构建及分析研究(Construction and Analysis of Chinese Multi-Target Sentiment Classification Dataset)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-nlp-models-via-contrast-sets-1","title":"Evaluating NLP Models via Contrast Sets","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-ceng-ci-zhu-yi-li-ji-zhi-he-men-ji-zhi","title":"基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jie-he-jin-rong-ling-yu-qing-gan-ci-dian-he","title":"结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-sentiment-analysis-with-multi","title":"Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wen-ben-qing-gan-fen-xi-zhong-de-zhong-die","title":"文本情感分析中的重叠现象研究(A Study on Repetition in Text-based Sentiment Analysis)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lebanonuprising-a-thorough-study-of-lebanese","title":"LEBANONUPRISING: a thorough study of Lebanese tweets","date":"2020-09-30","arxiv_id":"2009.14459","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantal-synaptic-dilution-enhances-sparse","title":"Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks","date":"2020-09-28","arxiv_id":"2009.13165","repositories_listed":0,"syntology":null},{"url":null,"slug":"metaphor-detection-using-deep-contextualized","title":"Metaphor Detection using Deep Contextualized Word Embeddings","date":"2020-09-26","arxiv_id":"2009.12565","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-extraction-of-agriculture-terms","title":"Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques","date":"2020-09-24","arxiv_id":"2009.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"subjective-metrics-based-cloud-market","title":"Subjective Metrics-based Cloud Market Performance Prediction","date":"2020-09-21","arxiv_id":"2009.09794","repositories_listed":0,"syntology":null},{"url":null,"slug":"wessa-at-semeval-2020-task-9-code-mixed","title":"WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers","date":"2020-09-21","arxiv_id":"2009.09879","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-approach-of-intention-discovery","title":"An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management","date":"2020-09-20","arxiv_id":"2009.09354","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-attack-towards-textual","title":"Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations","date":"2020-09-19","arxiv_id":"2009.09192","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-opinion-mining-using-a-hybrid","title":"Arabic Opinion Mining Using a Hybrid Recommender System Approach","date":"2020-09-16","arxiv_id":"2009.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-bi-lstm-performance-for-indonesian","title":"Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector","date":"2020-09-12","arxiv_id":"2009.05720","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularised-text-logistic-regression-key-word","title":"Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews","date":"2020-09-09","arxiv_id":"2009.04591","repositories_listed":0,"syntology":null},{"url":null,"slug":"kk2018-at-semeval-2020-task-9-adversarial","title":"kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification","date":"2020-09-08","arxiv_id":"2009.03673","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-bert-a-phrase-and-product-knowledge","title":"E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce","date":"2020-09-07","arxiv_id":"2009.02835","repositories_listed":0,"syntology":null}],"record_sha256":"e30a70dec5c06fe7ea729ced4a3207736c4925400e88a661f7e82ca905f53771","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}