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 951–1000 of 5630 papers

TitleStatusHype
Cardiovascular Disease Risk Prediction via Social Media—0
AnglE-optimized Text EmbeddingsCode2
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision—0
Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
In-Context Learning for Text Classification with Many Labels—0
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings—0
How People Perceive The Dynamic Zero-COVID Policy: A Retrospective Analysis From The Perspective of Appraisal Theory—0
Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis Using U.S. College Subreddit Data from 2019 to 2022Code0
Self-training Strategies for Sentiment Analysis: An Empirical Study—0
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary TasksCode0
Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts—0
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling—0
Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR EvaluationCode0
USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech DatasetCode0
GRASS: Unified Generation Model for Speech-to-Semantic Tasks—0
A deep Natural Language Inference predictor without language-specific training data—0
Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies—0
Exchanging-based Multimodal Fusion with TransformerCode1
Fine-grained Affective Processing Capabilities Emerging from Large Language Models—0
UniSA: Unified Generative Framework for Sentiment AnalysisCode1
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training—0
Studying the impacts of pre-training using ChatGPT-generated text on downstream tasks—0
Will sentiment analysis need subculture? A new data augmentation approachCode0
Linking microblogging sentiments to stock price movement: An application of GPT-4—0
Interpreting Sentiment Composition with Latent Semantic TreeCode0
HAlf-MAsked Model for Named Entity Sentiment analysis—0
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts—0
A Wide Evaluation of ChatGPT on Affective Computing TasksCode0
LSTM-based QoE Evaluation for Web Microservices' Reputation Scoring—0
Exploiting Diverse Feature for Multimodal Sentiment Analysis—0
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment ClassificationCode1
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment AnalysisCode0
To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis—0
Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT—0
Reinforcement Learning in Financial Markets: A Study on Dynamic Model Weight AssignmentCode0
Automated Testing and Improvement of Named Entity Recognition Systems—0
Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models—0
Transforming Sentiment Analysis in the Financial Domain with ChatGPTCode1
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM—0
A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset—0
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition—0
Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support—0
AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education—0
Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion MiningCode0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction—0
Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning ModelsCode0
Contextual Emotion Recognition Using Transformer-Based ModelsCode0
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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