{"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/12","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":12,"pages_in_order":57,"rows_per_page":100,"rows":[1101,1200],"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/11","next":"/task/sentiment-analysis/papers/13","papers":[{"url":"/paper/multi-label-sentiment-analysis-on-100","slug":"multi-label-sentiment-analysis-on-100","title":"Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance","date":"2020-08-26","arxiv_id":"2008.11573","repositories_listed":1,"syntology":null},{"url":"/paper/simple-unsupervised-similarity-based-aspect","slug":"simple-unsupervised-similarity-based-aspect","title":"Simple Unsupervised Similarity-Based Aspect Extraction","date":"2020-08-25","arxiv_id":"2008.10820","repositories_listed":1,"syntology":null},{"url":"/paper/textdecepter-hard-label-black-box-attack-on","slug":"textdecepter-hard-label-black-box-attack-on","title":"TextDecepter: Hard Label Black Box Attack on Text Classifiers","date":"2020-08-16","arxiv_id":"2008.06860","repositories_listed":1,"syntology":null},{"url":"/paper/jointly-fine-tuning-bert-like-self-supervised-1","slug":"jointly-fine-tuning-bert-like-self-supervised-1","title":"Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition","date":"2020-08-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-language-interpretability-tool-extensible","slug":"the-language-interpretability-tool-extensible","title":"The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models","date":"2020-08-12","arxiv_id":"2008.05122","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-language-interpretability-tool-extensible#ran","syntology_url":"https://syntology.ai/paper/2008.05122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05122"}},"official":{"repos":["PAIR-code/lit"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-neural-generative-model-for-joint-learning","slug":"a-neural-generative-model-for-joint-learning","title":"A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings","date":"2020-08-11","arxiv_id":"2008.04702","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-based-multi-person-multi","slug":"sentiment-analysis-based-multi-person-multi","title":"Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews","date":"2020-07-31","arxiv_id":"2008.00032","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-of-fasttext-word-embedding-effects-in","slug":"a-study-of-fasttext-word-embedding-effects-in","title":"A Study of fastText Word Embedding Effects in Document Classification in Bangla Language","date":"2020-07-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-results-on-russian-sentiment","slug":"improving-results-on-russian-sentiment","title":"Improving Results on Russian Sentiment Datasets","date":"2020-07-28","arxiv_id":"2007.14310","repositories_listed":1,"syntology":null},{"url":"/paper/ynu-hpcc-at-semeval-2020-task-8-using-a","slug":"ynu-hpcc-at-semeval-2020-task-8-using-a","title":"YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis","date":"2020-07-28","arxiv_id":"2007.13968","repositories_listed":1,"syntology":null},{"url":"/paper/fissa-at-semeval-2020-task-9-fine-tuned-for","slug":"fissa-at-semeval-2020-task-9-fine-tuned-for","title":"FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings","date":"2020-07-24","arxiv_id":"2007.12544","repositories_listed":1,"syntology":null},{"url":"/paper/hcms-at-semeval-2020-task-9-a-neural-approach","slug":"hcms-at-semeval-2020-task-9-a-neural-approach","title":"HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts","date":"2020-07-23","arxiv_id":"2007.12076","repositories_listed":1,"syntology":null},{"url":"/paper/baksa-at-semeval-2020-task-9-bolstering-cnn","slug":"baksa-at-semeval-2020-task-9-bolstering-cnn","title":"BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text","date":"2020-07-21","arxiv_id":"2007.10819","repositories_listed":1,"syntology":null},{"url":"/paper/iitk-at-semeval-2020-task-8-unimodal-and","slug":"iitk-at-semeval-2020-task-8-unimodal-and","title":"IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes","date":"2020-07-21","arxiv_id":"2007.10822","repositories_listed":1,"syntology":null},{"url":"/paper/mono-vs-multilingual-transformer-based-models","slug":"mono-vs-multilingual-transformer-based-models","title":"Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks","date":"2020-07-19","arxiv_id":"2007.09757","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-interaction-networks-with","slug":"hierarchical-interaction-networks-with","title":"Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis","date":"2020-07-16","arxiv_id":"2007.08445","repositories_listed":1,"syntology":null},{"url":"/paper/towards-debiasing-sentence-representations-1","slug":"towards-debiasing-sentence-representations-1","title":"Towards Debiasing Sentence Representations","date":"2020-07-16","arxiv_id":"2007.08100","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-debiasing-sentence-representations-1#ran","syntology_url":"https://syntology.ai/paper/2007.08100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08100"}},"official":{"repos":["pliang279/sent_debias"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-can-we-learn-from-almost-a-decade-of","slug":"what-can-we-learn-from-almost-a-decade-of","title":"What Can We Learn From Almost a Decade of Food Tweets","date":"2020-07-10","arxiv_id":"2007.05194","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-detection-of-sexist-statements","slug":"automatic-detection-of-sexist-statements","title":"Automatic Detection of Sexist Statements Commonly Used at the Workplace","date":"2020-07-08","arxiv_id":"2007.04181","repositories_listed":1,"syntology":null},{"url":"/paper/emotiongif-yankee-a-sentiment-classifier-with","slug":"emotiongif-yankee-a-sentiment-classifier-with","title":"EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods","date":"2020-07-05","arxiv_id":"2007.02259","repositories_listed":1,"syntology":null},{"url":"/paper/ch-sims-a-chinese-multimodal-sentiment","slug":"ch-sims-a-chinese-multimodal-sentiment","title":"CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modelling-context-and-syntactical-features","slug":"modelling-context-and-syntactical-features","title":"Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multilogue-net-a-context-aware-rnn-for-multi-2","slug":"multilogue-net-a-context-aware-rnn-for-multi-2","title":"Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/relation-aware-collaborative-learning-for","slug":"relation-aware-collaborative-learning-for","title":"Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/synchronous-double-channel-recurrent-network","slug":"synchronous-double-channel-recurrent-network","title":"Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-transformer-based-joint-encoding-for-1","slug":"a-transformer-based-joint-encoding-for-1","title":"A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis","date":"2020-06-29","arxiv_id":"2006.15955","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-transformer-based-joint-encoding-for-1#ran","syntology_url":"https://syntology.ai/paper/2006.15955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15955"}},"official":null}},{"url":"/paper/tweetscov19-a-knowledge-base-of-semantically","slug":"tweetscov19-a-knowledge-base-of-semantically","title":"TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic","date":"2020-06-25","arxiv_id":"2006.14492","repositories_listed":1,"syntology":null},{"url":"/paper/can-you-tell-ssnet-a-sagittal-stratum","slug":"can-you-tell-ssnet-a-sagittal-stratum","title":"Can you tell? SSNet -- a Sagittal Stratum-inspired Neural Network Framework for Sentiment Analysis","date":"2020-06-23","arxiv_id":"2006.12958","repositories_listed":1,"syntology":null},{"url":"/paper/comparative-sentiment-analysis-of-app-reviews","slug":"comparative-sentiment-analysis-of-app-reviews","title":"Comparative Sentiment Analysis of App Reviews","date":"2020-06-17","arxiv_id":"2006.09739","repositories_listed":1,"syntology":null},{"url":"/paper/perl-pivot-based-domain-adaptation-for-pre","slug":"perl-pivot-based-domain-adaptation-for-pre","title":"PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models","date":"2020-06-16","arxiv_id":"2006.09075","repositories_listed":1,"syntology":null},{"url":"/paper/memesem-a-multi-modal-framework-for","slug":"memesem-a-multi-modal-framework-for","title":"MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer Learning","date":"2020-06-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cs-embed-francesita-at-semeval-2020-task-9","slug":"cs-embed-francesita-at-semeval-2020-task-9","title":"CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis","date":"2020-06-08","arxiv_id":"2006.04597","repositories_listed":1,"syntology":null},{"url":"/paper/aspect-based-sentiment-analysis-of-scientific","slug":"aspect-based-sentiment-analysis-of-scientific","title":"Aspect-based Sentiment Analysis of Scientific Reviews","date":"2020-06-05","arxiv_id":"2006.03257","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-dual-view-model-for-review","slug":"a-unified-dual-view-model-for-review","title":"A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss","date":"2020-06-02","arxiv_id":"2006.01592","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/a-unified-dual-view-model-for-review#ran","syntology_url":"https://syntology.ai/paper/2006.01592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.01592"}},"official":{"repos":["kenchan0226/dual_view_review_sum"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/an-effectiveness-metric-for-ordinal","slug":"an-effectiveness-metric-for-ordinal","title":"An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results","date":"2020-06-01","arxiv_id":"2006.01245","repositories_listed":1,"syntology":null},{"url":"/paper/covid-19-social-media-sentiment-analysis-on","slug":"covid-19-social-media-sentiment-analysis-on","title":"COVID-19: Social Media Sentiment Analysis on Reopening","date":"2020-06-01","arxiv_id":"2006.00804","repositories_listed":1,"syntology":null},{"url":"/paper/a-sentiment-analysis-dataset-for-code-mixed-1","slug":"a-sentiment-analysis-dataset-for-code-mixed-1","title":"A Sentiment Analysis Dataset for Code-Mixed Malayalam-English","date":"2020-05-30","arxiv_id":"2006.00210","repositories_listed":1,"syntology":null},{"url":"/paper/corpus-creation-for-sentiment-analysis-in-1","slug":"corpus-creation-for-sentiment-analysis-in-1","title":"Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text","date":"2020-05-30","arxiv_id":"2006.00206","repositories_listed":1,"syntology":null},{"url":"/paper/language-representation-models-for-fine","slug":"language-representation-models-for-fine","title":"Language Representation Models for Fine-Grained Sentiment Classification","date":"2020-05-27","arxiv_id":"2005.13619","repositories_listed":1,"syntology":null},{"url":"/paper/symptom-extraction-from-the-narratives-of","slug":"symptom-extraction-from-the-narratives-of","title":"Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit","date":"2020-05-21","arxiv_id":"2005.10454","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-word-embeddings-for-turkic","slug":"cross-lingual-word-embeddings-for-turkic","title":"Cross-Lingual Word Embeddings for Turkic Languages","date":"2020-05-17","arxiv_id":"2005.08340","repositories_listed":1,"syntology":null},{"url":"/paper/activation-functions-are-not-needed-the-ratio","slug":"activation-functions-are-not-needed-the-ratio","title":"Activation functions are not needed: the ratio net","date":"2020-05-14","arxiv_id":"2005.06678","repositories_listed":1,"syntology":null},{"url":"/paper/a-sentiwordnet-strategy-for-curriculum","slug":"a-sentiwordnet-strategy-for-curriculum","title":"A SentiWordNet Strategy for Curriculum Learning in Sentiment Analysis","date":"2020-05-10","arxiv_id":"2005.04749","repositories_listed":1,"syntology":null},{"url":"/paper/impactcite-an-xlnet-based-method-for-citation","slug":"impactcite-an-xlnet-based-method-for-citation","title":"ImpactCite: An XLNet-based method for Citation Impact Analysis","date":"2020-05-05","arxiv_id":"2005.06611","repositories_listed":1,"syntology":null},{"url":"/paper/opiniondigest-a-simple-framework-for-opinion","slug":"opiniondigest-a-simple-framework-for-opinion","title":"OpinionDigest: A Simple Framework for Opinion Summarization","date":"2020-05-05","arxiv_id":"2005.01901","repositories_listed":1,"syntology":null},{"url":"/paper/kingdom-knowledge-guided-domain-adaptation","slug":"kingdom-knowledge-guided-domain-adaptation","title":"KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis","date":"2020-05-02","arxiv_id":"2005.00791","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/kingdom-knowledge-guided-domain-adaptation#ran","syntology_url":"https://syntology.ai/paper/2005.00791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00791"}},"official":{"repos":["declare-lab/kingdom"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/beneath-the-tip-of-the-iceberg-current","slug":"beneath-the-tip-of-the-iceberg-current","title":"Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research","date":"2020-05-01","arxiv_id":"2005.00357","repositories_listed":1,"syntology":null},{"url":"/paper/camel-tools-an-open-source-python-toolkit-for","slug":"camel-tools-an-open-source-python-toolkit-for","title":"CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/defense-of-word-level-adversarial-attacks-via","slug":"defense-of-word-level-adversarial-attacks-via","title":"Defense of Word-level Adversarial Attacks via Random Substitution Encoding","date":"2020-05-01","arxiv_id":"2005.00446","repositories_listed":1,"syntology":null},{"url":"/paper/klej-comprehensive-benchmark-for-polish","slug":"klej-comprehensive-benchmark-for-polish","title":"KLEJ: Comprehensive Benchmark for Polish Language Understanding","date":"2020-05-01","arxiv_id":"2005.00630","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/klej-comprehensive-benchmark-for-polish#ran","syntology_url":"https://syntology.ai/paper/2005.00630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00630"}},"official":{"repos":["allegro/klejbenchmark-baselines"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/training-a-broad-coverage-german-sentiment","slug":"training-a-broad-coverage-german-sentiment","title":"Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-focused-study-to-compare-arabic-pre","slug":"a-focused-study-to-compare-arabic-pre","title":"An Empirical Study of Pre-trained Transformers for Arabic Information Extraction","date":"2020-04-30","arxiv_id":"2004.14519","repositories_listed":1,"syntology":null},{"url":"/paper/muse-2020-the-first-international-multimodal","slug":"muse-2020-the-first-international-multimodal","title":"MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop","date":"2020-04-30","arxiv_id":"2004.14858","repositories_listed":1,"syntology":null},{"url":"/paper/perturbed-masking-parameter-free-probing-for","slug":"perturbed-masking-parameter-free-probing-for","title":"Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT","date":"2020-04-30","arxiv_id":"2004.14786","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/perturbed-masking-parameter-free-probing-for#ran","syntology_url":"https://syntology.ai/paper/2004.14786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14786"}},"official":{"repos":["LividWo/Perturbed-Masking"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/user-guided-aspect-classification-for-domain","slug":"user-guided-aspect-classification-for-domain","title":"User-Guided Aspect Classification for Domain-Specific Texts","date":"2020-04-30","arxiv_id":"2004.14555","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-multimodal-routing-for-human","slug":"interpretable-multimodal-routing-for-human","title":"Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis","date":"2020-04-29","arxiv_id":"2004.14198","repositories_listed":1,"syntology":null},{"url":"/paper/subjqa-a-dataset-for-subjectivity-and-review","slug":"subjqa-a-dataset-for-subjectivity-and-review","title":"SubjQA: A Dataset for Subjectivity and Review Comprehension","date":"2020-04-29","arxiv_id":"2004.14283","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/subjqa-a-dataset-for-subjectivity-and-review#ran","syntology_url":"https://syntology.ai/paper/2004.14283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14283"}},"official":{"repos":["megagonlabs/SubjQA"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/dombert-domain-oriented-language-model-for","slug":"dombert-domain-oriented-language-model-for","title":"DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis","date":"2020-04-28","arxiv_id":"2004.13816","repositories_listed":1,"syntology":null},{"url":"/paper/embarrassingly-simple-unsupervised-aspect","slug":"embarrassingly-simple-unsupervised-aspect","title":"Embarrassingly Simple Unsupervised Aspect Extraction","date":"2020-04-28","arxiv_id":"2004.13580","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/embarrassingly-simple-unsupervised-aspect#ran","syntology_url":"https://syntology.ai/paper/2004.13580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13580"}},"official":{"repos":["clips/cat"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-graph-attention-network-for-aspect","slug":"relational-graph-attention-network-for-aspect","title":"Relational Graph Attention Network for Aspect-based Sentiment Analysis","date":"2020-04-26","arxiv_id":"2004.12362","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":1,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/relational-graph-attention-network-for-aspect#ran","syntology_url":"https://syntology.ai/paper/2004.12362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12362"}},"official":{"repos":["shenwzh3/RGAT-ABSA"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/train-no-evil-selective-masking-for-task","slug":"train-no-evil-selective-masking-for-task","title":"Train No Evil: Selective Masking for Task-Guided Pre-Training","date":"2020-04-21","arxiv_id":"2004.09733","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/train-no-evil-selective-masking-for-task#ran","syntology_url":"https://syntology.ai/paper/2004.09733","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.09733"}},"official":{"repos":["thunlp/SelectiveMasking"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-pharmacovigilance-with-drug-reviews","slug":"enhancing-pharmacovigilance-with-drug-reviews","title":"Enhancing Pharmacovigilance with Drug Reviews and Social Media","date":"2020-04-18","arxiv_id":"2004.08731","repositories_listed":1,"syntology":null},{"url":"/paper/how-recurrent-networks-implement-contextual","slug":"how-recurrent-networks-implement-contextual","title":"How recurrent networks implement contextual processing in sentiment analysis","date":"2020-04-17","arxiv_id":"2004.08013","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/how-recurrent-networks-implement-contextual#ran","syntology_url":"https://syntology.ai/paper/2004.08013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08013"}},"official":null}},{"url":"/paper/sentiment-analysis-of-arabic-algerian-dialect","slug":"sentiment-analysis-of-arabic-algerian-dialect","title":"Sentiment Analysis of Arabic Algerian Dialect Using a Supervised Method","date":"2020-04-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-of-yelp-reviews-a","slug":"sentiment-analysis-of-yelp-reviews-a","title":"Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models","date":"2020-04-15","arxiv_id":"2004.13851","repositories_listed":1,"syntology":null},{"url":"/paper/multi-source-attention-for-unsupervised","slug":"multi-source-attention-for-unsupervised","title":"Multi-source Attention for Unsupervised Domain Adaptation","date":"2020-04-14","arxiv_id":"2004.06608","repositories_listed":1,"syntology":null},{"url":"/paper/classification-benchmarks-for-under-resourced","slug":"classification-benchmarks-for-under-resourced","title":"Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network","date":"2020-04-11","arxiv_id":"2004.07807","repositories_listed":1,"syntology":null},{"url":"/paper/deepsentipers-novel-deep-learning-models","slug":"deepsentipers-novel-deep-learning-models","title":"DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus","date":"2020-04-11","arxiv_id":"2004.05328","repositories_listed":1,"syntology":null},{"url":"/paper/increasing-the-inference-and-learning-speed","slug":"increasing-the-inference-and-learning-speed","title":"Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing","date":"2020-04-07","arxiv_id":"2004.03188","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-nlp-models-via-contrast-sets","slug":"evaluating-nlp-models-via-contrast-sets","title":"Evaluating Models' Local Decision Boundaries via Contrast Sets","date":"2020-04-06","arxiv_id":"2004.02709","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-strategic-behavior-from-free-text","slug":"predicting-strategic-behavior-from-free-text","title":"Predicting Strategic Behavior from Free Text","date":"2020-04-06","arxiv_id":"2004.02973","repositories_listed":1,"syntology":null},{"url":"/paper/give-your-text-representation-models-some","slug":"give-your-text-representation-models-some","title":"Give your Text Representation Models some Love: the Case for Basque","date":"2020-03-31","arxiv_id":"2004.00033","repositories_listed":1,"syntology":null},{"url":"/paper/cost-sensitive-bert-for-generalisable-1","slug":"cost-sensitive-bert-for-generalisable-1","title":"Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data","date":"2020-03-16","arxiv_id":"2003.11563","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-encode-position-for-transformer","slug":"learning-to-encode-position-for-transformer","title":"Learning to Encode Position for Transformer with Continuous Dynamical Model","date":"2020-03-13","arxiv_id":"2003.09229","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-to-encode-position-for-transformer#ran","syntology_url":"https://syntology.ai/paper/2003.09229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.09229"}},"official":null}},{"url":"/paper/sememnn-a-semantic-matrix-based-memory-neural","slug":"sememnn-a-semantic-matrix-based-memory-neural","title":"SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification","date":"2020-03-04","arxiv_id":"2003.01857","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-typed-syntactic-dependencies-for","slug":"exploiting-typed-syntactic-dependencies-for","title":"Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network","date":"2020-02-22","arxiv_id":"2002.09685","repositories_listed":1,"syntology":null},{"url":"/paper/kryptooracle-a-real-time-cryptocurrency-price","slug":"kryptooracle-a-real-time-cryptocurrency-price","title":"KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments","date":"2020-02-21","arxiv_id":"2003.04967","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-models-vs-transfer-learning-for","slug":"hierarchical-models-vs-transfer-learning-for","title":"A Systematic Comparison of Architectures for Document-Level Sentiment Classification","date":"2020-02-19","arxiv_id":"2002.08131","repositories_listed":1,"syntology":null},{"url":"/paper/multilogue-net-a-context-aware-rnn-for-multi","slug":"multilogue-net-a-context-aware-rnn-for-multi","title":"Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation","date":"2020-02-19","arxiv_id":"2002.08267","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multilogue-net-a-context-aware-rnn-for-multi#ran","syntology_url":"https://syntology.ai/paper/2002.08267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08267"}},"official":{"repos":["amanshenoy/multilogue-net"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/robustness-verification-for-transformers-1","slug":"robustness-verification-for-transformers-1","title":"Robustness Verification for Transformers","date":"2020-02-16","arxiv_id":"2002.06622","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/robustness-verification-for-transformers-1#ran","syntology_url":"https://syntology.ai/paper/2002.06622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.06622"}},"official":{"repos":["shizhouxing/Robustness-Verification-for-Transformers"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-detection-of-subjective-bias-using","slug":"towards-detection-of-subjective-bias-using","title":"Towards Detection of Subjective Bias using Contextualized Word Embeddings","date":"2020-02-16","arxiv_id":"2002.06644","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-using-averaged-weighted","slug":"sentiment-analysis-using-averaged-weighted","title":"Sentiment Analysis Using Averaged Weighted Word Vector Features","date":"2020-02-13","arxiv_id":"2002.05606","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-bert-intermediate-layers-for-aspect","slug":"utilizing-bert-intermediate-layers-for-aspect","title":"Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference","date":"2020-02-12","arxiv_id":"2002.04815","repositories_listed":1,"syntology":null},{"url":"/paper/improving-domain-adapted-sentiment","slug":"improving-domain-adapted-sentiment","title":"Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning","date":"2020-02-01","arxiv_id":"2002.00119","repositories_listed":1,"syntology":null},{"url":"/paper/sequence-labeling-approach-to-the-task-of","slug":"sequence-labeling-approach-to-the-task-of","title":"Sequence Labeling Approach to the Task of Sentence Boundary Detection","date":"2020-01-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-sentiment-analysis-for-code","slug":"unsupervised-sentiment-analysis-for-code","title":"Unsupervised Sentiment Analysis for Code-mixed Data","date":"2020-01-20","arxiv_id":"2001.11384","repositories_listed":1,"syntology":null},{"url":"/paper/robbert-a-dutch-roberta-based-language-model","slug":"robbert-a-dutch-roberta-based-language-model","title":"RobBERT: a Dutch RoBERTa-based Language Model","date":"2020-01-17","arxiv_id":"2001.06286","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robbert-a-dutch-roberta-based-language-model#ran","syntology_url":"https://syntology.ai/paper/2001.06286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.06286"}},"official":{"repos":["iPieter/RobBERT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/predictive-analysis-of-bitcoin-price","slug":"predictive-analysis-of-bitcoin-price","title":"Predictive analysis of Bitcoin price considering social sentiments","date":"2020-01-16","arxiv_id":"2001.10343","repositories_listed":1,"syntology":null},{"url":"/paper/latent-opinions-transfer-network-for-target","slug":"latent-opinions-transfer-network-for-target","title":"Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction","date":"2020-01-07","arxiv_id":"2001.01989","repositories_listed":1,"syntology":null},{"url":"/paper/generating-word-and-document-embeddings-for","slug":"generating-word-and-document-embeddings-for","title":"Generating Word and Document Embeddings for Sentiment Analysis","date":"2020-01-05","arxiv_id":"2001.01269","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-deep-learning-for-sentiment","slug":"adapting-deep-learning-for-sentiment","title":"Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text","date":"2020-01-04","arxiv_id":"2001.01047","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-paraphrase-question-generator","slug":"revisiting-paraphrase-question-generator","title":"Revisiting Paraphrase Question Generator using Pairwise Discriminator","date":"2019-12-31","arxiv_id":"1912.13149","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-predictive-model-for-stock-price","slug":"a-robust-predictive-model-for-stock-price","title":"A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing","date":"2019-12-09","arxiv_id":"1912.07700","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-aspect-clustering-for-sentiment","slug":"multilingual-aspect-clustering-for-sentiment","title":"Multilingual aspect clustering for sentiment analysis","date":"2019-12-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-emotion-classification-of","slug":"fine-grained-emotion-classification-of","title":"Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks","date":"2019-12-05","arxiv_id":"1912.02545","repositories_listed":1,"syntology":null},{"url":"/paper/semeval-2016-task-4-sentiment-analysis-in-1","slug":"semeval-2016-task-4-sentiment-analysis-in-1","title":"SemEval-2016 Task 4: Sentiment Analysis in Twitter","date":"2019-12-03","arxiv_id":"1912.01973","repositories_listed":1,"syntology":null},{"url":"/paper/a-fine-grained-sentiment-dataset-for","slug":"a-fine-grained-sentiment-dataset-for","title":"A Fine-Grained Sentiment Dataset for Norwegian","date":"2019-11-28","arxiv_id":"1911.12722","repositories_listed":1,"syntology":null},{"url":"/paper/low-rank-factorization-for-compact-multi-head","slug":"low-rank-factorization-for-compact-multi-head","title":"Low Rank Factorization for Compact Multi-Head Self-Attention","date":"2019-11-26","arxiv_id":"1912.00835","repositories_listed":1,"syntology":null},{"url":"/paper/causally-denoise-word-embeddings-using-half","slug":"causally-denoise-word-embeddings-using-half","title":"Causally Denoise Word Embeddings Using Half-Sibling Regression","date":"2019-11-24","arxiv_id":"1911.10524","repositories_listed":1,"syntology":null},{"url":"/paper/a-transformer-based-approach-to-irony-and","slug":"a-transformer-based-approach-to-irony-and","title":"A Transformer-based approach to Irony and Sarcasm detection","date":"2019-11-23","arxiv_id":"1911.10401","repositories_listed":1,"syntology":null}],"record_sha256":"1c914dc24c898c89e3dbc62954975b35fa11e5d299f4b818aed5b29dffc8c093","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}