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 2001–2050 of 5630 papers

TitleStatusHype
Semi-Supervised Learning Based on Auto-generated Lexicon Using XAI in Sentiment Analysis—0
Sentiment Analysis in Code-Mixed Telugu-English Text with Unsupervised Data Normalization—0
Semantic-Based Opinion SummarizationCode0
Lexicon-based Sentiment Analysis in German: Systematic Evaluation of Resources and Preprocessing TechniquesCode0
Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach—0
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment AnalysisCode1
The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment datasetCode0
Discretized Integrated Gradients for Explaining Language ModelsCode1
Improving Multimodal fusion via Mutual Dependency Maximisation—0
Cross-Lingual Text Classification of Transliterated Hindi and MalayalamCode0
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation—0
CAPE: Context-Aware Private Embeddings for Private Language LearningCode0
Using GAN-based models to sentimental analysis on imbalanced datasets in education domain—0
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal ExplanationsCode1
Prompt-Learning for Fine-Grained Entity Typing—0
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word EmbeddingsCode0
Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence—0
SERF: Towards better training of deep neural networks using log-Softplus ERror activation Function—0
MOFit: A Framework to reduce Obesity using Machine learning and IoT—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
A Weakly Supervised Dataset of Fine-Grained Emotions in PortugueseCode0
Graph Capsule Aggregation for Unaligned Multimodal SequencesCode0
MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving FactorsCode0
Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis—0
FiLMing Multimodal Sarcasm Detection with AttentionCode0
Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection—0
Sentiment Analysis on the News to Improve Mental Health—0
Recommending Insurance products by using Users' Sentiments—0
Bambara Language Dataset for Sentiment AnalysisCode0
Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory NetworkCode0
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space TranslationCode1
sarcasm detection and quantification in arabic tweets—0
Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment—0
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition—0
Interactive Reinforcement Learning for Table Balancing Robot—0
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis—0
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis—0
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis—0
UserAdapter: Few-Shot User Learning in Sentiment Analysis—0
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis—0
MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in TransformersCode1
Sentiment-based Candidate Selection for NMTCode0
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework—0
基于时间注意力胶囊网络的维吾尔语情感分类模型(Uyghur Sentiment Classification Model Based on Temporal Attention Capsule Networks)—0
融合情感分析的隐式反问句识别模型(Implicit Rhetorical Questions Recognition Model Combined with Sentiment Analysis)—0
Enhancing Aspect Extraction for Hindi—0
Evaluating morphological typology in zero-shot cross-lingual transfer—0
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification—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