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 2901–2950 of 5630 papers

TitleStatusHype
Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations—0
ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning—0
Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information—0
Enhancing Aspect Extraction for Hindi—0
Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions—0
Enhancing Financial Sentiment Analysis with Expert-Designed Hint—0
Enhancing General Sentiment Lexicons for Domain-Specific Use—0
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models—0
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis—0
Enhancing Lexicon-Based Review Classification by Merging and Revising Sentiment Dictionaries—0
Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content—0
Enhancing Multi-Modal Video Sentiment Classification Through Semi-Supervised Clustering—0
Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development—0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
Enhancing Root Extractors Using Light Stemmers—0
Enhancing search engine precision and user experience through sentiment-based polysemy resolution—0
Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies—0
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought—0
Advancing Aspect-Based Sentiment Analysis through Deep Learning Models—0
Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering—0
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content—0
Ensemble BERT: A student social network text sentiment classification model based on ensemble learning and BERT architecture—0
Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction—0
Ensemble Language Models for Multilingual Sentiment Analysis—0
Ensemble Technique Utilization for Indonesian Dependency Parser—0
Entertainment chatbot for the digital inclusion of elderly people without abstraction capabilities—0
Entities' Sentiment Relevance—0
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis—0
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations—0
Entity Centric Opinion Mining from Blogs—0
Entity-centric Sentiment Analysis on Twitter data for the Potuguese Language—0
Entity/Event-Level Sentiment Detection and Inference—0
Entity Hierarchy Embedding—0
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models—0
Entity-level Sentiment Analysis in Contact Center Telephone Conversations—0
Entity Linking on Microblogs with Spatial and Temporal Signals—0
Entity Retrieval and Text Mining for Online Reputation Monitoring—0
Entity-Specific Sentiment Classification of Yahoo News Comments—0
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
Equity Beyond Bias in Language Technologies for Education—0
ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors—0
ERUPD -- English to Roman Urdu Parallel Dataset—0
ESG Sentiment Analysis: comparing human and language model performance including GPT—0
Estimating Quality in Therapeutic Conversations: A Multi-Dimensional Natural Language Processing Framework—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—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