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 3001–3050 of 5630 papers

TitleStatusHype
Resources and Experiments on Sentiment Classification for Georgian—0
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data—0
Response Construct Tagging: NLP-Aided Assessment for Engineering Education—0
ResTS : Syst\`eme de R\'esum\'e Automatique des Textes d'Opinions bas\'e sur Twitter et SentiWordNet (System of Customer Review Summarization using Twitter and SentiWordNet) [in French]—0
Rethinking Annotation: Can Language Learners Contribute?—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture—0
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies—0
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities—0
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN—0
Re-tweeting from a linguistic perspective—0
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics—0
Review-Level Sentiment Classification with Sentence-Level Polarity Correction—0
Review Mining for Feature Based Opinion Summarization and Visualization—0
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing—0
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques—0
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning—0
Right-truncatable Neural Word Embeddings—0
RiskFinder: A Sentence-level Risk Detector for Financial Reports—0
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks—0
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis—0
RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis—0
RoBERT -- A Romanian BERT Model—0
Robust Cross-Domain Sentiment Analysis for Low-Resource Languages—0
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text—0
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks—0
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation—0
Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions—0
Robustness of Large Language Models Against Adversarial Attacks—0
Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi—0
Robust Task Clustering for Deep Many-Task Learning—0
ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts—0
Romanization-based Approach to Morphological Analysis in Korean SMS Text Processing—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
RoseMerry: A Baseline Message-level Sentiment Classification System—0
RRA: Recurrent Residual Attention for Sequence Learning—0
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages—0
Ruchi: Rating Individual Food Items in Restaurant Reviews—0
Rude waiter but mouthwatering pastries! An exploratory study into Dutch Aspect-Based Sentiment Analysis—0
Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts—0
Rule-based Coreference Resolution in German Historic Novels—0
Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel—0
Rule-Based Sentiment Analysis in Narrow Domain: Detecting Sentiment in Daily Horoscopes Using Sentiscope—0
Rule-Based Weibo Messages Sentiment Polarity Classification towards Given Topics—0
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
RVISA: Reasoning and Verification for Implicit Sentiment Analysis—0
Sa‘7r: A Saudi Dialect Irony Dataset—0
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm—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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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