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 3551–3600 of 5630 papers

TitleStatusHype
Iterative Recursive Attention Model for Interpretable Sequence Classification—0
"I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data—0
It’s absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?—0
It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Comments—0
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter—0
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines—0
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention—0
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation—0
JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)—0
基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)—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
基于异构用户知识融合的隐式情感分析研究(Research on Implicit Sentiment Analysis based on Heterogeneous User Knowledge Fusion)—0
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest—0
Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints—0
Joint Learning for Targeted Sentiment Analysis—0
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification—0
Joint Learning of Sense and Word Embeddings—0
Jointly Learning to Embed and Predict with Multiple Languages—0
Joint Named Entity Recognition and Stance Detection in Tweets—0
Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models—0
Joint sentiment analysis of lyrics and audio in music—0
Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine—0
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees—0
JU\_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis—0
JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts—0
JU\_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines—0
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation—0
JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation—0
KanCMD: Kannada CodeMixed Dataset for Sentiment Analysis and Offensive Language Detection—0
KDE-AFFECT at SemEval-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gram—0
Kea: Expression-level Sentiment Analysis from Twitter Data—0
Kea: Sentiment Analysis of Phrases Within Short Texts—0
Keeping in Time: Adding Temporal Context to Sentiment Analysis Models—0
KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter—0
KeLP: a Kernel-based Learning Platform for Natural Language Processing—0
KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification—0
Kernel Density Estimation for Multiclass Quantification—0
KESA: A Knowledge Enhanced Approach For Sentiment Analysis—0
Key-phrase boosted unsupervised summary generation for FinTech organization—0
Khmer Text Classification Using Word Embedding and Neural Networks—0
KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis—0
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification—0
KLUEless: Polarity Classification and Association—0
KLUE: Simple and robust methods for polarity classification—0
Knowledge Adaptation: Teaching to Adapt—0
Model Aggregation via Good-Enough Model Spaces—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