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 1801–1850 of 5630 papers

TitleStatusHype
Economic Event Detection in Company-Specific News Text—0
An Iterative Algorithm for Rescaled Hyperbolic Functions Regression—0
Benchmarking Twitter Sentiment Analysis Tools—0
Combining Argument Mining Techniques—0
EDEN: Evolutionary Deep Networks for Efficient Machine Learning—0
EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture—0
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction—0
EDUCE: Explaining model Decision through Unsupervised Concepts Extraction—0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis—0
Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting—0
Effective Attention Modeling for Aspect-Level Sentiment Classification—0
Effective Black Box Testing of Sentiment Analysis Classification Networks—0
Effective Few-Shot Classification with Transfer Learning—0
Effectively Leveraging BERT for Legal Document Classification—0
English Event Detection With Translated Language Features—0
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis—0
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory—0
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Effect of Using Regression on Class Confidence Scores in Sentiment Analysis of Twitter Data—0
Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales—0
Effects of padding on LSTMs and CNNs—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games—0
+/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference—0
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction—0
Efficient Feature Selection techniques for Sentiment Analysis—0
BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis—0
Efficient Models for the Detection of Hate, Abuse and Profanity—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis—0
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification—0
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability—0
Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism—0
Efficient Twitter Sentiment Classification using Subjective Distant Supervision—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Annotating Opinions in German Political News—0
Egyptian Dialect Stopword List Generation from Social Network Data—0
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification—0
A Linguistically Informed Convolutional Neural Network—0
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
Electoral Programs of German Parties 2021: A Computational Analysis Of Their Comprehensibility and Likeability Based On SentiArt—0
Enabling Complex Wikipedia Queries - Technical Report—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
A short note on estimating intelligence from user profiles in the context of universal psychometrics: prospects and caveats—0
ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015—0
Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media—0
ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets—0
EliXa: A Modular and Flexible ABSA Platform—0
A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis—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