{"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-classification/papers/7","list_of":"/task/sentiment-classification","task":"Sentiment 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":7,"pages_in_order":10,"rows_per_page":100,"rows":[601,700],"of":949,"counts":{"archive_papers_tagged":949,"with_a_code_link":332,"where_syntology_ran_a_sample":45,"not_listed_spam_title":0,"listed":949,"listed_where_code_ran":45,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":39,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":39,"listed_every_run_a_failure_of_syntologys_instrument":6,"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-classification","prev":"/task/sentiment-classification/papers/6","next":"/task/sentiment-classification/papers/8","papers":[{"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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},{"url":null,"slug":"the-impact-of-indirect-machine-translation-on","title":"The Impact of Indirect Machine Translation on Sentiment Classification","date":"2020-08-25","arxiv_id":"2008.11257","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcr-net-a-deep-co-interactive-relation","title":"DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification","date":"2020-08-16","arxiv_id":"2008.06914","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-extracting-functions-for-neural-logic","title":"Feature Extraction Functions for Neural Logic Rule Learning","date":"2020-08-14","arxiv_id":"2008.06326","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-bert-solve-commonsense-task-via","title":"On Commonsense Cues in BERT for Solving Commonsense Tasks","date":"2020-08-10","arxiv_id":"2008.03945","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-lda-and-lstm-models-to-study-public","title":"Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019","date":"2020-08-01","arxiv_id":"2008.07366","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-of-text-processing-steps-on-twitter","title":"Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding","date":"2020-07-25","arxiv_id":"2007.13027","repositories_listed":0,"syntology":null},{"url":null,"slug":"tweets-sentiment-analysis-via-word-embeddings","title":"Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques","date":"2020-07-05","arxiv_id":"2007.04303","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidirectional-encoder-representations-from","title":"Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey","date":"2020-07-02","arxiv_id":"2007.01127","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-and-domain-aware-bert-for-cross","title":"Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-sentiment-classification-with-document","title":"Aspect Sentiment Classification with Document-level Sentiment Preference Modeling","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"corpus-based-amharic-sentiment-lexicon-1","title":"Corpus based Amharic sentiment lexicon generation","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-unsupervised-sentiment","title":"Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-graph-enhanced-dual-transformer","title":"Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-projection-for-improved-text","title":"Feature Projection for Improved Text Classification","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"negation-handling-for-amharic-sentiment","title":"Negation handling for Amharic sentiment classification","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentitel-tabsa-for-twitter-reviews-on-uganda","title":"SentiTel: TABSA for Twitter reviews on Uganda Telecoms","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weight-poisoning-attacks-on-pretrained-models","title":"Weight Poisoning Attacks on Pretrained Models","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iit-gandhinagar-at-semeval-2020-task-9-code","title":"IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection","date":"2020-06-25","arxiv_id":"2006.14465","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-for-sentiment","title":"Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features","date":"2020-06-01","arxiv_id":"2006.04680","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":"article-citation-study-context-enhanced","title":"Article citation study: Context enhanced citation sentiment detection","date":"2020-05-10","arxiv_id":"2005.04534","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-citations-of-natural-language","title":"Examining Citations of Natural Language Processing Literature","date":"2020-05-02","arxiv_id":"2005.00912","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-annotation-framework-for-luxembourgish","title":"An Annotation Framework for Luxembourgish Sentiment Analysis","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-indian-language-social-media-collection","title":"An Indian Language Social Media Collection for Hate and Offensive Speech","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-domain-polarity-changes-of-words-in","title":"Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon","date":"2020-04-29","arxiv_id":"2004.14357","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdtsc-crowd-based-neural-networks-for-text","title":"CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification","date":"2020-04-26","arxiv_id":"2004.12389","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sentiment-classification-and-topic","title":"Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach","date":"2020-04-24","arxiv_id":"2004.11695","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-review-comprehension-with-domain","title":"Enhancing Review Comprehension with Domain-Specific Commonsense","date":"2020-04-06","arxiv_id":"2004.03020","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-enrichment-of-nigerian-pidgin","title":"Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification","date":"2020-03-27","arxiv_id":"2003.12450","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-sentiment-1","title":"Convolutional Neural Networks for Sentiment Analysis in Persian Social Media","date":"2020-02-14","arxiv_id":"2002.06233","repositories_listed":0,"syntology":null},{"url":null,"slug":"related-tasks-can-share-a-multi-task","title":"Related Tasks can Share! A Multi-task Framework for Affective language","date":"2020-02-06","arxiv_id":"2002.02154","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-tiled-convolutional-neural-networks","title":"Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification","date":"2020-01-31","arxiv_id":"2001.11857","repositories_listed":0,"syntology":null},{"url":"/paper/multi-source-domain-adaptation-for-visual","slug":"multi-source-domain-adaptation-for-visual","title":"Multi-source Domain Adaptation for Visual Sentiment Classification","date":"2020-01-12","arxiv_id":"2001.03886","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-classification-using","title":"Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data","date":"2020-01-03","arxiv_id":"2001.00994","repositories_listed":0,"syntology":null},{"url":null,"slug":"corpus-based-amharic-sentiment-lexicon","title":"Corpus Based Amharic Sentiment Lexicon Generation","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"target-guided-structured-attention-network","title":"Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-identification-of-tweet-purpose","title":"Simultaneous Identification of Tweet Purpose and Position","date":"2019-12-24","arxiv_id":"2001.00051","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-outcome-prediction-using-sentiment","title":"Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds","date":"2019-12-11","arxiv_id":"1912.05066","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-deep-learning-based-sentiment","title":"Robust Deep Learning Based Sentiment Classification of Code-Mixed Text","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"log-message-anomaly-detection-and","title":"Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU","date":"2019-11-20","arxiv_id":"1911.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-versus-traditional-classifiers","title":"Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus","date":"2019-11-17","arxiv_id":"1911.07223","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-recurrent-units-for-cloze-style","title":"Contextual Recurrent Units for Cloze-style Reading Comprehension","date":"2019-11-14","arxiv_id":"1911.05960","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-unsupervised","title":"A Comparative Analysis of Unsupervised Language Adaptation Methods","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-self-learning-framework-for-cross","title":"A Robust Self-Learning Framework for Cross-Lingual Text Classification","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ensemble-of-humour-sarcasm-and-hate","title":"An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-level-sentiment-analysis-via","title":"Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"capsule-network-with-interactive-attention","title":"Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-dynamic-routing-in-tree","title":"Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexicalat-lexical-based-adversarial","title":"LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-stance-detection-with-sentiment","title":"Multi-Task Stance Detection with Sentiment and Stance Lexicons","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-conflict-opinions-in-aspect-level","title":"Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"to-annotate-or-not-predicting-performance","title":"To Annotate or Not? Predicting Performance Drop under Domain Shift","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-discourse-structure-using-distant","title":"Predicting Discourse Structure using Distant Supervision from Sentiment","date":"2019-10-30","arxiv_id":"1910.14176","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013425","title":"Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification","date":"2019-10-29","arxiv_id":"1910.13425","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-attention-based-graph-convolutional","title":"Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification","date":"2019-10-24","arxiv_id":"1910.10857","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-like-decision-making-document-level","title":"Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning","date":"2019-10-21","arxiv_id":"1910.09260","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-for-machines-the","title":"Machine Translation for Machines: the Sentiment Classification Use Case","date":"2019-10-01","arxiv_id":"1910.00478","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-attention-networks-for-fine","title":"Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health","date":"2019-09-30","arxiv_id":"1910.00054","repositories_listed":0,"syntology":null},{"url":null,"slug":"amharic-negation-handling","title":"Amharic Negation Handling","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-adversarial-co-learning-for-multi-domain","title":"Dual Adversarial Co-Learning for Multi-Domain Text Classification","date":"2019-09-18","arxiv_id":"1909.08203","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-black-box-adversarial-examples-for","title":"Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model","date":"2019-09-17","arxiv_id":"1909.07873","repositories_listed":0,"syntology":null},{"url":"/paper/parameterized-convolutional-neural-networks-1","slug":"parameterized-convolutional-neural-networks-1","title":"Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification","date":"2019-09-13","arxiv_id":"1909.06276","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-interpretability-of-neural","title":"Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation","date":"2019-09-10","arxiv_id":"1909.04225","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-aware-aspect-level-sentiment","title":"Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks","date":"2019-09-05","arxiv_id":"1909.02606","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-sentiment-classification-using","title":"Cross-Domain Sentiment Classification using Vector Embedded Domain Representations","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-noisy-labels-for-sentence-level","title":"Learning with Noisy Labels for Sentence-level Sentiment Classification","date":"2019-08-31","arxiv_id":"1909.00124","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-invariant-feature-distillation-for","title":"Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification","date":"2019-08-24","arxiv_id":"1908.09122","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-few-shot-text-classification-via","title":"When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification","date":"2019-08-22","arxiv_id":"1908.08788","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-sentiment-analysis-with-faithful","title":"Fine-grained Sentiment Analysis with Faithful Attention","date":"2019-08-19","arxiv_id":"1908.06870","repositories_listed":0,"syntology":null},{"url":null,"slug":"tdam-a-topic-dependent-attention-model-for","title":"TDAM: a Topic-Dependent Attention Model for Sentiment Analysis","date":"2019-08-18","arxiv_id":"1908.06435","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-evolutionary-approach-to-bioinspired","title":"A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction","date":"2019-08-13","arxiv_id":"1908.04784","repositories_listed":0,"syntology":null},{"url":"/paper/structbert-incorporating-language-structures","slug":"structbert-incorporating-language-structures","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","date":"2019-08-13","arxiv_id":"1908.04577","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attack-on-sentiment","title":"Adversarial Attack on Sentiment Classification","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-location-and-text-features-for","title":"Fusing location and text features for sentiment classification","date":"2019-07-28","arxiv_id":"1907.12008","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-shot-classification-a-comparison-of","title":"Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches","date":"2019-07-17","arxiv_id":"1907.07543","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-sentiment-analysis-using-deep","title":"Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis","date":"2019-07-15","arxiv_id":"1907.08696","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-as-explicit-word-based-rules","title":"Neural Networks as Explicit Word-Based Rules","date":"2019-07-10","arxiv_id":"1907.04613","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-based-classification","title":"Deep neural network-based classification model for Sentiment Analysis","date":"2019-07-03","arxiv_id":"1907.02046","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-sentiment-classification-towards","title":"Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-bayesian-natural-language-processing","title":"Deep Bayesian Natural Language Processing","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-political-herd-mentality-a","title":"Investigating Political Herd Mentality: A Community Sentiment Based Approach","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reverse-engineering-recurrent-networks-for","title":"Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics","date":"2019-06-25","arxiv_id":"1906.10720","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effect-of-word-order-on-cross-lingual","title":"On the Effect of Word Order on Cross-lingual Sentiment Analysis","date":"2019-06-13","arxiv_id":"1906.05889","repositories_listed":0,"syntology":null}],"record_sha256":"39eb890372196f1e52b43c9003c9243fdeb9f9b940f5f5f7dfddacd4bc38da27","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}