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 2201–2250 of 5630 papers

TitleStatusHype
Feature Selection for Highly Skewed Sentiment Analysis Tasks—0
Feature Selection as Causal Inference: Experiments with Text Classification—0
A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis—0
From Image to Text in Sentiment Analysis via Regression and Deep Learning—0
Feature Projection for Improved Text Classification—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
Character-to-Character Sentiment Analysis in Shakespeare's Plays—0
Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.—0
Feature-level Rating System using Customer Reviews and Review Votes—0
Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines—0
Challenges of Evaluating Sentiment Analysis Tools on Social Media—0
Few-Shot Spoken Language Understanding via Joint Speech-Text Models—0
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network—0
Feature-Frequency--Adaptive On-line Training for Fast and Accurate Natural Language Processing—0
Feature Extraction Functions for Neural Logic Rule Learning—0
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps—0
Challenges in the development of annotated corpora of computer-mediated communication in Indian Languages: A Case of Hindi—0
FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN—0
Chinese Evaluative Information Analysis—0
Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach—0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Financial Keyword Expansion via Continuous Word Vector Representations—0
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets—0
Financial Sentiment Analysis for Risk Prediction—0
Chinese Irony Corpus Construction and Ironic Structure Analysis—0
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT—0
Financial sentiment analysis using FinBERT with application in predicting stock movement—0
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
A review of sentiment computation methods with R packages—0
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics—0
Chinese Sentiments on the Clouds: A Preliminary Experiment on Corpus Processing and Exploration on Cloud Service—0
FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis—0
Finding fake reviews in e-commerce platforms by using hybrid algorithms—0
Feature based Sentiment Analysis using a Domain Ontology—0
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers—0
Challenges in modality annotation in a Brazilian Portuguese Spontaneous Speech Corpus—0
Fine-grained Affective Processing Capabilities Emerging from Large Language Models—0
Fine-Grained Arabic Dialect Identification—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
CIDER: Context sensitive sentiment analysis for short-form text—0
Fine-grained Financial Opinion Mining: A Survey and Research Agenda—0
Fine-grained German Sentiment Analysis on Social Media—0
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Fine-Grained Opinion Summarization with Minimal Supervision—0
Fine-Grained Sentiment Analysis for Movie Reviews in Bulgarian—0
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities—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
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