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 2651–2700 of 5630 papers

TitleStatusHype
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals—0
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density—0
Identifying Sentiments in Algerian Code-switched User-generated Comments—0
Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews—0
Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks—0
Improving Distributed Representations of Tweets - Present and Future—0
Improving Document-Level Sentiment Classification Using Importance of Sentences—0
A Multidisciplinary Approach to Telegram Data Analysis—0
Identifying Political Sentiment between Nation States with Social Media—0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories—0
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration—0
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions—0
Improving Formality Style Transfer with Context-Aware Rule Injection—0
Identifying negativity factors from social media text corpus using sentiment analysis method—0
Identifying Intention Posts in Discussion Forums—0
Aspect-based Sentiment Analysis with Opinion Tree Generation—0
Identifying High-Impact Sub-Structures for Convolution Kernels in Document-level Sentiment Classification—0
Identifying Emotion Labels from Psychiatric Social Texts Using Independent Component Analysis—0
Identifying Adversarial Attacks on Text Classifiers—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Identification of the Breach of Short-term Rental Regulations in Irish Rent Pressure Zones—0
Context-Enhanced Citation Sentiment Detection—0
A Multi-Dimensional Bayesian Approach to Lexical Style—0
Identification of emotions on Twitter during the 2022 electoral process in Colombia—0
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Context-aware Sentiment Word Identification: sentiword2vec—0
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic—0
IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News—0
Improving Sentiment Analysis in Arabic Using Word Representation—0
Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data—0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Improving Sentiment Analysis with Biofeedback Data—0
Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization—0
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations—0
"I ain't tellin' white folks nuthin": A quantitative exploration of the race-related problem of candour in the WPA slave narratives—0
Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes—0
Context-aware Fine-tuning of Self-supervised Speech Models—0
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems—0
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
A multiclass Q-NLP sentiment analysis experiment using DisCoCat—0
A Deep Learning System for Sentiment Analysis of Service Calls—0
A deep-learning framework to detect sarcasm targets—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts—0
Examining Structure of Word Embeddings with PCA—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