{"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/21","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":21,"pages_in_order":57,"rows_per_page":100,"rows":[2001,2100],"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/20","next":"/task/sentiment-analysis/papers/22","papers":[{"url":null,"slug":"ensemble-language-models-for-multilingual","title":"Ensemble Language Models for Multilingual Sentiment Analysis","date":"2024-03-10","arxiv_id":"2403.06060","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grainedly-synthesize-streaming-data","title":"Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity","date":"2024-03-10","arxiv_id":"2403.06139","repositories_listed":0,"syntology":null},{"url":null,"slug":"persian-slang-text-conversion-to-formal-and","title":"Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification","date":"2024-03-09","arxiv_id":"2403.06023","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multimodal-sentiment-analysis","title":"Towards Multimodal Sentiment Analysis Debiasing via Bias Purification","date":"2024-03-08","arxiv_id":"2403.05023","repositories_listed":0,"syntology":null},{"url":null,"slug":"macms-magahi-code-mixed-dataset-for-sentiment","title":"MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis","date":"2024-03-07","arxiv_id":"2403.04639","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tutorial-on-the-pretrain-finetune-paradigm","title":"A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing","date":"2024-03-04","arxiv_id":"2403.02504","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-text-sentiment-analysis-reinforcing","title":"Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis","date":"2024-03-04","arxiv_id":"2403.01921","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilled-chatgpt-topic-sentiment-modeling","title":"Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance","date":"2024-03-04","arxiv_id":"2403.02185","repositories_listed":0,"syntology":null},{"url":null,"slug":"kenet-knowledge-enhanced-doc-label-attention","title":"KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification","date":"2024-03-04","arxiv_id":"2403.01767","repositories_listed":0,"syntology":null},{"url":null,"slug":"subjective-textit-isms-on-the-danger-of","title":"Subjective $\\textit{Isms}$? On the Danger of Conflating Hate and Offence in Abusive Language Detection","date":"2024-03-04","arxiv_id":"2403.02268","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-cross-language-framework-for","title":"A comprehensive cross-language framework for harmful content detection with the aid of sentiment analysis","date":"2024-03-02","arxiv_id":"2403.01270","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-for-targeted-sentiment-in-news-headlines","title":"LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma","date":"2024-03-01","arxiv_id":"2403.00418","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-in-political-science-heralding-a-new-era","title":"LLMs in Political Science: Heralding a New Era of Visual Analysis","date":"2024-02-29","arxiv_id":"2403.00154","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-intrinsic-dimension-via-information","title":"Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis","date":"2024-02-28","arxiv_id":"2402.18145","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-nostalgia-detection-from-bengali","title":"Automatic Nostalgia Detection from Bengali Text","date":"2024-02-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"esg-sentiment-analysis-comparing-human-and","title":"ESG Sentiment Analysis: comparing human and language model performance including GPT","date":"2024-02-26","arxiv_id":"2402.16650","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-effective-ensembles-for-sentiment","title":"Generating Effective Ensembles for Sentiment Analysis","date":"2024-02-26","arxiv_id":"2402.16700","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-approach-to-detect-2","title":"A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters","date":"2024-02-25","arxiv_id":"2402.15992","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-understanding-and-generation-using","title":"Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations","date":"2024-02-25","arxiv_id":"2402.16035","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabiangpt-native-arabic-gpt-based-large","title":"ArabianGPT: Native Arabic GPT-based Large Language Model","date":"2024-02-23","arxiv_id":"2402.15313","repositories_listed":0,"syntology":null},{"url":null,"slug":"carbd-ko-a-contextually-annotated-review","title":"CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean","date":"2024-02-23","arxiv_id":"2402.15046","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-encoder-exploiting-the-potential-of","title":"Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction","date":"2024-02-23","arxiv_id":"2402.15370","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-generalization-via-causal-adjustment","title":"Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis","date":"2024-02-22","arxiv_id":"2402.14536","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-adoption-to-adaption-tracing-the","title":"From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter","date":"2024-02-22","arxiv_id":"2402.14187","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-with-industrial-lens-deciphering-the","title":"LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey","date":"2024-02-22","arxiv_id":"2402.14558","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-adaptive-contextual-masking-for","title":"Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis","date":"2024-02-21","arxiv_id":"2402.13722","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-news-and-media-sentiment-analysis","title":"Applying News and Media Sentiment Analysis for Generating Forex Trading Signals","date":"2024-02-19","arxiv_id":"2403.00785","repositories_listed":0,"syntology":null},{"url":null,"slug":"emoji-driven-crypto-assets-market-reactions","title":"Emoji Driven Crypto Assets Market Reactions","date":"2024-02-16","arxiv_id":"2402.10481","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-bias-calibration-for-better-zero","title":"Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models","date":"2024-02-15","arxiv_id":"2402.10353","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-nlp-models-with-strategic-text","title":"Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies","date":"2024-02-14","arxiv_id":"2402.09141","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-weibo-platform-experts-perform-better-at","title":"Do Weibo platform experts perform better at predicting stock market?","date":"2024-02-12","arxiv_id":"2403.00772","repositories_listed":0,"syntology":null},{"url":null,"slug":"fabert-pre-training-bert-on-persian-blogs","title":"FaBERT: Pre-training BERT on Persian Blogs","date":"2024-02-09","arxiv_id":"2402.06617","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-models-for-the-detection-of-hate","title":"Efficient Models for the Detection of Hate, Abuse and Profanity","date":"2024-02-08","arxiv_id":"2402.05624","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-context-learning-can-re-learn-forbidden","title":"In-Context Learning Can Re-learn Forbidden Tasks","date":"2024-02-08","arxiv_id":"2402.05723","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-for-open","title":"Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses","date":"2024-02-07","arxiv_id":"2402.04812","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-topic-modelling-approaches-in","title":"Comparison of Topic Modelling Approaches in the Banking Context","date":"2024-02-05","arxiv_id":"2402.03176","repositories_listed":0,"syntology":null},{"url":null,"slug":"homograph-attacks-on-maghreb-sentiment","title":"Homograph Attacks on Maghreb Sentiment Analyzers","date":"2024-02-05","arxiv_id":"2402.03171","repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistic-features-for-sentence-difficulty","title":"Linguistic features for sentence difficulty prediction in ABSA","date":"2024-02-05","arxiv_id":"2402.03163","repositories_listed":0,"syntology":null},{"url":null,"slug":"sidu-txt-an-xai-algorithm-for-nlp-with-a","title":"SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach","date":"2024-02-05","arxiv_id":"2402.03043","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-quality-matters-suicide-intention","title":"Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN","date":"2024-02-03","arxiv_id":"2402.02262","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-in-non-fixed-length-audios","title":"Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network","date":"2024-02-03","arxiv_id":"2402.02184","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-sentiment-analysis-in-low-resource","title":"Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon","date":"2024-02-03","arxiv_id":"2402.02113","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-market-sentiment-based-ensemble","title":"Learning the Market: Sentiment-Based Ensemble Trading Agents","date":"2024-02-02","arxiv_id":"2402.01441","repositories_listed":0,"syntology":null},{"url":null,"slug":"pics-pipeline-for-image-captioning-and-search","title":"PICS: Pipeline for Image Captioning and Search","date":"2024-02-01","arxiv_id":"2402.10090","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-tweet-act-a-weighted-ensemble-pre","title":"Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter","date":"2024-01-30","arxiv_id":"2401.17373","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-transformer-based-encoder-for","title":"Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks","date":"2024-01-30","arxiv_id":"2401.17396","repositories_listed":0,"syntology":null},{"url":null,"slug":"cerm-context-aware-literature-based-discovery","title":"CERM: Context-aware Literature-based Discovery via Sentiment Analysis","date":"2024-01-27","arxiv_id":"2402.01724","repositories_listed":0,"syntology":null},{"url":null,"slug":"bloom-epistemic-and-sentiment-analysis","title":"Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums","date":"2024-01-26","arxiv_id":"2402.01716","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-adaptation-for-financial","title":"Large Language Model Adaptation for Financial Sentiment Analysis","date":"2024-01-26","arxiv_id":"2401.14777","repositories_listed":0,"syntology":null},{"url":null,"slug":"techniques-to-detect-crime-leaders-within-a","title":"Unlocking Criminal Hierarchies: A Survey, Experimental, and Comparative Exploration of Techniques for Identifying Leaders within Criminal Networks","date":"2024-01-26","arxiv_id":"2402.03355","repositories_listed":0,"syntology":null},{"url":null,"slug":"tweet-influence-on-market-trends-analyzing","title":"Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks","date":"2024-01-26","arxiv_id":"2402.03353","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-portability-of-parameter","title":"Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods","date":"2024-01-25","arxiv_id":"2401.14228","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatgpt-vs-gemini-vs-llama-on-multilingual","title":"ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysis","date":"2024-01-25","arxiv_id":"2402.01715","repositories_listed":0,"syntology":null},{"url":null,"slug":"romansetu-efficiently-unlocking-multilingual","title":"RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization","date":"2024-01-25","arxiv_id":"2401.14280","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-view-of-the-biases-of","title":"A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions","date":"2024-01-23","arxiv_id":"2401.12720","repositories_listed":0,"syntology":null},{"url":null,"slug":"longitudinal-sentiment-classification-of","title":"Longitudinal Sentiment Classification of Reddit Posts","date":"2024-01-22","arxiv_id":"2401.12382","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-preservation-property-in-knowledge","title":"Confidence Preservation Property in Knowledge Distillation Abstractions","date":"2024-01-21","arxiv_id":"2401.11365","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-robust-multimodal-learning-using","title":"Toward Robust Multimodal Learning using Multimodal Foundational Models","date":"2024-01-20","arxiv_id":"2401.13697","repositories_listed":0,"syntology":null},{"url":null,"slug":"biofinbert-finetuning-large-language-models","title":"BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks","date":"2024-01-19","arxiv_id":"2401.11011","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-colonial-impulse-of-natural-language","title":"The \"Colonial Impulse\" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases","date":"2024-01-19","arxiv_id":"2401.10535","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-multi-objective-optimization-of-1","title":"Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments","date":"2024-01-18","arxiv_id":"2401.09862","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-severity-of-dental-and-oral","title":"Estimating the severity of dental and oral problems via sentiment classification over clinical reports","date":"2024-01-17","arxiv_id":"2401.12993","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-approach-for-ethereum-price","title":"Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis","date":"2024-01-16","arxiv_id":"2401.08077","repositories_listed":0,"syntology":null},{"url":null,"slug":"milestones-in-bengali-sentiment-analysis","title":"Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions","date":"2024-01-15","arxiv_id":"2401.07847","repositories_listed":0,"syntology":null},{"url":null,"slug":"stability-analysis-of-chatgpt-based-sentiment","title":"Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance","date":"2024-01-15","arxiv_id":"2401.07441","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-the-emotional-generation-capability","title":"Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought","date":"2024-01-12","arxiv_id":"2401.06836","repositories_listed":0,"syntology":null},{"url":null,"slug":"wisdom-improving-multimodal-sentiment","title":"WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge","date":"2024-01-12","arxiv_id":"2401.06659","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-heterogeneous-llm-agents-for","title":"Designing Heterogeneous LLM Agents for Financial Sentiment Analysis","date":"2024-01-11","arxiv_id":"2401.05799","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-unsupervised-semantic-document","title":"Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis","date":"2024-01-11","arxiv_id":"2401.06210","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for-dialects-of-a","title":"Natural Language Processing for Dialects of a Language: A Survey","date":"2024-01-11","arxiv_id":"2401.05632","repositories_listed":0,"syntology":null},{"url":null,"slug":"we-need-to-talk-about-classification","title":"We Need to Talk About Classification Evaluation Metrics in NLP","date":"2024-01-08","arxiv_id":"2401.03831","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-augmented-global-contrast-for","title":"Contextual Augmented Global Contrast for Multimodal Intent Recognition","date":"2024-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-and-utilization-methods-of-domain","title":"Fine-tuning and Utilization Methods of Domain-specific LLMs","date":"2024-01-01","arxiv_id":"2401.02981","repositories_listed":0,"syntology":null},{"url":null,"slug":"mart-masked-affective-representation-learning","title":"MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation","date":"2024-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-density-estimation-for-multiclass","title":"Kernel Density Estimation for Multiclass Quantification","date":"2023-12-31","arxiv_id":"2401.00490","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-is-all-you-need-prompting","title":"Evaluation is all you need. Prompting Generative Large Language Models for Annotation Tasks in the Social Sciences. A Primer using Open Models","date":"2023-12-30","arxiv_id":"2401.00284","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiding-in-plain-sight-towards-the-science-of","title":"Hiding in Plain Sight: Towards the Science of Linguistic Steganography","date":"2023-12-28","arxiv_id":"2312.16840","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-sentiment-analysis-with-missing","title":"Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach","date":"2023-12-28","arxiv_id":"2401.10747","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-category-learning-and-sentimental","title":"Aspect category learning and sentimental analysis using weakly supervised learning","date":"2023-12-24","arxiv_id":"2312.15526","repositories_listed":0,"syntology":null},{"url":null,"slug":"paralinguistics-enhanced-large-language","title":"Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue","date":"2023-12-23","arxiv_id":"2312.15316","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-llm-bias-index-llmbi","title":"Large Language Model (LLM) Bias Index -- LLMBI","date":"2023-12-22","arxiv_id":"2312.14769","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-contextual-target-attributes-for","title":"Exploiting Contextual Target Attributes for Target Sentiment Classification","date":"2023-12-21","arxiv_id":"2312.13766","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-quantifying-sentiments-of-financial-news","title":"On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?","date":"2023-12-21","arxiv_id":"2312.14978","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-multimodal-sentiment-analysis-on","title":"Explainable Multimodal Sentiment Analysis on Bengali Memes","date":"2023-12-20","arxiv_id":"2401.09446","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-public-reactions-perceptions-and","title":"Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets","date":"2023-12-19","arxiv_id":"2312.11895","repositories_listed":0,"syntology":null},{"url":null,"slug":"geo-located-aspect-based-sentiment-analysis","title":"Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments","date":"2023-12-19","arxiv_id":"2312.12253","repositories_listed":0,"syntology":null},{"url":null,"slug":"powmix-a-versatile-regularizer-for-multimodal","title":"PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis","date":"2023-12-19","arxiv_id":"2312.12334","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-with-explicit","title":"Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations","date":"2023-12-18","arxiv_id":"2312.10961","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-and-text-analysis-of-the","title":"Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox","date":"2023-12-17","arxiv_id":"2312.10580","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-soft-contrastive-learning-based-prompt","title":"A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis","date":"2023-12-16","arxiv_id":"2312.10479","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-context-aware-fine-tuning-of-self","title":"Generative Context-aware Fine-tuning of Self-supervised Speech Models","date":"2023-12-15","arxiv_id":"2312.09895","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-fine-tuned-llms-and","title":"A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis","date":"2023-12-14","arxiv_id":"2312.08725","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-sentiment-classification","title":"Deep Learning-based Sentiment Classification: A Comparative Survey","date":"2023-12-12","arxiv_id":"2312.17253","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-sentiment-analysis-perceived-vs","title":"Multimodal Sentiment Analysis: Perceived vs Induced Sentiments","date":"2023-12-12","arxiv_id":"2312.07627","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-in-tourism-fine-tuning","title":"Sentiment analysis in Tourism: Fine-tuning BERT or sentence embeddings concatenation?","date":"2023-12-12","arxiv_id":"2312.07797","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-smoothing-for-enhanced-text-sentiment","title":"Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification","date":"2023-12-11","arxiv_id":"2312.06522","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-friendly-and-adaptable-discriminative-ai","title":"User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models","date":"2023-12-11","arxiv_id":"2312.06826","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceiving-university-student-s-opinions-from","title":"Perceiving University Student's Opinions from Google App Reviews","date":"2023-12-10","arxiv_id":"2312.06705","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-hybrid-and-ensemble-in-deep","title":"A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing","date":"2023-12-09","arxiv_id":"2312.05589","repositories_listed":0,"syntology":null},{"url":null,"slug":"corporate-bankruptcy-prediction-with-domain","title":"Corporate Bankruptcy Prediction with Domain-Adapted BERT","date":"2023-12-06","arxiv_id":"2312.03194","repositories_listed":0,"syntology":null}],"record_sha256":"bafc271f70e7f5889e0ed87801c07b28fdd05e2aa3e999b422137b40f9057591","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}