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 2301–2350 of 5630 papers

TitleStatusHype
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis—0
Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP ModelsCode0
Sentiment-based Candidate Selection for NMTCode0
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
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis—0
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis—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
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework—0
UMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word EmbeddingsCode0
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis—0
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages—0
Gates Are Not What You Need in RNNsCode0
UserAdapter: Few-Shot User Learning in Sentiment Analysis—0
Geolocation differences of language use in urban areas—0
Opinion Prediction with User FingerprintingCode0
Sentiment Analysis of the COVID-related r/Depression Posts—0
Arabic aspect sentiment polarity classification using BERT—0
Compensation Learning—0
Preliminary Steps Towards Federated Sentiment Classification—0
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network—0
Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic—0
Spinning Sequence-to-Sequence Models with Meta-Backdoors—0
Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests—0
Impacts Towards a comprehensive assessment of the book impact by integrating multiple evaluation sources—0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
Stock price prediction using BERT and GAN—0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews—0
A Robust Deep Ensemble Classifier for Figurative Language Detection—0
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in TweetsCode0
Identifying negativity factors from social media text corpus using sentiment analysis method—0
Transfer Learning for Improving Results on Russian Sentiment DatasetsCode0
Sarcasm Detection: A Comparative Study—0
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Cross-lingual alignments of ELMo contextual embeddings—0
Current Landscape of the Russian Sentiment Corpora—0
Sentiment analysis for Urdu online reviews using deep learning modelsCode0
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects—0
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language—0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
Sequential Late Fusion Technique for Multi-modal Sentiment Analysis—0
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment ClassificationCode0
Explicit Interaction Network for Aspect Sentiment Triplet Extraction—0
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis—0
Hybrid approach to detecting symptoms of depression in social media entries—0
How COVID-19 Has Changed Crowdfunding: Evidence From GoFundMe—0
Towards Financial Sentiment Analysis in a South African Landscape—0
BadNL: Backdoor Attacks Against NLP Models—0
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model—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