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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1150 of 5630 papers

TitleStatusHype
A Statistical Parsing Framework for Sentiment Classification—0
Analyse de sentiments \`a base d'aspects par combinaison de r\'eseaux profonds : application \`a des avis en fran (A combination of deep learning methods for aspect-based sentiment analysis : application to French reviews)—0
Compositional Distributional Models of Meaning—0
Assigning Connotation Values to Events—0
Assessment of Massively Multilingual Sentiment Classifiers—0
An Algorithm for Routing Vectors in Sequences—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction—0
An Algorithm for Routing Capsules in All Domains—0
A Comparison of Automatic Labelling Approaches for Sentiment Analysis—0
Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Assessing Sentiment Strength in Words Prior Polarities—0
An Advanced Press Review System Combining Deep News Analysis and Machine Learning Algorithms—0
An Account of Opinion Implicatures—0
Assessing Objective Recommendation Quality through Political Forecasting—0
A Comparison of Approaches for Sentiment Classification on Lithuanian Internet Comments—0
Competing Independent Modules for Knowledge Integration and Optimization—0
Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis—0
A Multi-View Sentiment Corpus—0
Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding—0
Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks—0
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language—0
Adjective Intensity and Sentiment Analysis—0
Compensation Learning—0
Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network—0
A Distant Supervision Approach to Semantic Role Labeling—0
A Boosting-based Algorithm for Classification of Semi-Structured Text using the Frequency of Substructures—0
A multi-task learning network using shared BERT models for aspect-based sentiment analysis—0
Comparison of SVM Optimization Techniques in the Primal—0
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining—0
A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset—0
Comparison of Topic Modelling Approaches in the Banking Context—0
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset—0
Compositional De-Attention Networks—0
Computational Sarcasm—0
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling—0
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks—0
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network—0
A Dictionary-Based Approach to Identifying Aspects Implied by Adjectives for Opinion Mining—0
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition—0
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning—0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction—0
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network—0
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree—0
A Multi-task Approach to Predict Likability of Books—0
Show:102550
← PrevPage 23 of 113Next →
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