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 3501–3550 of 5630 papers

TitleStatusHype
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings—0
Real Time Monitoring of Social Media and Digital Press—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives—0
Argumentation Mining: Exploiting Multiple Sources and Background Knowledge—0
Dual Memory Network Model for Biased Product Review Classification—0
Emo2Vec: Learning Generalized Emotion Representation by Multi-task TrainingCode0
Sentiment analysis for Arabic language: A brief survey of approaches and techniques—0
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag RepresentationsCode0
Cell-aware Stacked LSTMs for Modeling Sentences—0
Multi-Source Domain Adaptation with Mixture of ExpertsCode0
Adaptive Semi-supervised Learning for Cross-domain Sentiment ClassificationCode0
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion ClassificationCode0
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural TherapyCode0
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs—0
Iterative Recursive Attention Model for Interpretable Sequence Classification—0
Identifying the sentiment styles of YouTube's vloggers—0
On Tree-Based Neural Sentence ModelingCode0
Distance Based Source Domain Selection for Sentiment Classification—0
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks—0
Convolutional Neural Networks with Recurrent Neural FiltersCode0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global InformationCode0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Revisiting the Importance of Encoding Logic Rules in Sentiment ClassificationCode0
Emoji Sentiment Scores of Writers using Odds Ratio and Fisher Exact Test—0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language RecognitionCode0
Exploiting Deep Learning for Persian Sentiment Analysis—0
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis—0
Multimodal Language Analysis with Recurrent Multistage FusionCode0
Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches—0
Sentimental Content Analysis and Knowledge Extraction from News Articles—0
Building a Kannada POS Tagger Using Machine Learning and Neural Network ModelsCode0
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies—0
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network—0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction—0
Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification—0
Towards Coreference for Literary Text: Analyzing Domain-Specific Phenomena—0
Fine-Grained Arabic Dialect Identification—0
Enhancing General Sentiment Lexicons for Domain-Specific Use—0
A review of Spanish corpora annotated with negationCode0
Distinguishing affixoid formations from compoundsCode0
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory—0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction—0
Effective Attention Modeling for Aspect-Level Sentiment Classification—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Task-oriented Word Embedding for Text ClassificationCode0
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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