SOTAVerified

Sentiment Analysis

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

Papers

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Showing 1701–1750 of 5630 papers

TitleStatusHype
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance—0
A New Approach To Text Rating Classification Using Sentiment Analysis—0
Do Large Language Models Possess Sensitive to Sentiment?—0
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering—0
A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis—0
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation—0
Domain Adaptation for Sentiment Analysis using Keywords in the Target Domain as the Learning Weight—0
`Aye' or `No'? Speech-level Sentiment Analysis of Hansard UK Parliamentary Debate Transcripts—0
Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping—0
Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback—0
Domain Adaptation Using a Combination of Multiple Embeddings for Sentiment Analysis—0
As Long as You Name My Name Right: Social Circles and Social Sentiment in the Hollywood Hearings—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population—0
BACN: Bi-direction Attention Capsule-based Network for Multimodal Sentiment Analysis—0
BadNL: Backdoor Attacks Against NLP Models—0
Combining Social Cognitive Theories with Linguistic Features for Multi-genre Sentiment Analysis—0
Domain Aggregation Networks for Multi-Source Domain Adaptation—0
Domain and Task-Informed Sample Selection for Cross-Domain Target-based Sentiment Analysis—0
Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics—0
領域相關詞彙極性分析及文件情緒分類之研究 (Domain Dependent Word Polarity Analysis for Sentiment Classification) [In Chinese]—0
A Benchmark for Text Quantification Learning Under Real-World Temporal Distribution Shift—0
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification—0
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
Domain Sentiment Matters: A Two Stage Sentiment Analyzer—0
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications—0
Domain-specific long text classification from sparse relevant information—0
Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks—0
Domain-Specific Sentiment Lexicons Induced from Labeled Documents—0
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
DomEx: Extraction of Sentiment Lexicons for Domains and Meta-Domains—0
Do Multi-Sense Embeddings Improve Natural Language Understanding?—0
Effective Few-Shot Classification with Transfer Learning—0
Do Neighbours Help? An Exploration of Graph-based Algorithms for Cross-domain Sentiment Classification—0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text—0
BAN-ABSA: An Aspect-Based Sentiment Analysis dataset for Bengali and it's baseline evaluation—0
Combining Minimally-supervised Methods for Arabic Named Entity Recognition—0
Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis—0
Do We Really Need Lexical Information? Towards a Top-down Approach to Sentiment Analysis of Product Reviews—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
A literature survey on student feedback assessment tools and their usage in sentiment analysis—0
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam—0
Drift to Remember—0
Effectively Leveraging BERT for Legal Document Classification—0
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All datasetsSST-2 Binary classificationIMDbSST-5 Fine-grained classificationYelp Binary classificationMRYelp Fine-grained classificationBanglaBookDynaSentSST-3Sentiment MergedUser and product informationAmazon Review Full

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100—Unverified
2MT-DNN-SMARTAccuracy97.5—Unverified
3T5-11BAccuracy97.5—Unverified
4MUPPET Roberta LargeAccuracy97.4—Unverified
5T5-3BAccuracy97.4—Unverified
6ALBERTAccuracy97.1—Unverified
7StructBERTRoBERTa ensembleAccuracy97.1—Unverified
8XLNet (single model)Accuracy97—Unverified
9SMARTRoBERTaDev Accuracy96.9—Unverified
10ELECTRAAccuracy96.9—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68—Unverified
2RoBERTa-largeAccuracy96.54—Unverified
3XLNetAccuracy96.21—Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2—Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1—Unverified
6GraphStarAccuracy96—Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94—Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92—Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9—Unverified
10BERT large finetune UDAAccuracy95.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27—Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2—Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8—Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1—Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07—Unverified
6Heinsen Routing + GPT-2Accuracy58.5—Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2—Unverified
8BERT LargeAccuracy55.5—Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9—Unverified
10BCN+ELMoAccuracy54.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88—Unverified
2SVDCNNError4.74—Unverified
3LEAMError4.69—Unverified
4fastText, h=10, bigramError4.3—Unverified
5SWEM-hierError4.19—Unverified
6SRNNError3.96—Unverified
7M-ACNNError3.89—Unverified
8DNC+CUWError3.6—Unverified
9CCCapsNetError3.52—Unverified
10Block-sparse LSTMError3.27—Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500—Unverified
2VLAWEAccuracy93.3—Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5—Unverified
4AnglE-LLaMA-7BAccuracy91.09—Unverified
5byte mLSTM7Accuracy86.8—Unverified
6MEANAccuracy84.5—Unverified
7RNN-CapsuleAccuracy83.8—Unverified
8Capsule-BAccuracy82.3—Unverified
9SuBiLSTM-TiedAccuracy81.6—Unverified
10USE_T+CNNAccuracy81.59—Unverified