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 2301–2350 of 5630 papers

TitleStatusHype
From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models—0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset—0
Challenges in modality annotation in a Brazilian Portuguese Spontaneous Speech Corpus—0
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities—0
From Image to Text in Sentiment Analysis via Regression and Deep Learning—0
From newspaper to microblogging: What does it take to find opinions?—0
FBK: Sentiment Analysis in Twitter with Tweetsted—0
Challenges in Creating a Multilingual Sentiment Analysis Application for Social Media Mining—0
A review of sentiment analysis research in Arabic language—0
From Review to Rating: Exploring Dependency Measures for Text Classification—0
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification—0
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews—0
Graph-based Semi-Supervised Learning Algorithms for NLP—0
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Fusing location and text features for sentiment classification—0
Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences—0
FBK: Exploiting Phrasal and Contextual Clues for Negation Scope Detection—0
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation—0
Gaining and Losing Influence in Online Conversation—0
Gated Convolutional Neural Networks for Domain Adaptation—0
Gated Mechanism for Attention Based Multimodal Sentiment Analysis—0
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents—0
Gender Prediction for Chinese Social Media Data—0
Gender stereotypes in the mediated personalization of politics: Empirical evidence from a lexical, syntactic and sentiment analysis—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation—0
Challenges for Open-domain Targeted Sentiment Analysis—0
Generalizable Multi-linear Attention Network—0
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction—0
Generalized Character-Level Spelling Error Correction—0
Generalized Sentiment-Bearing Expression Features for Sentiment Analysis—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
General Purpose Textual Sentiment Analysis and Emotion Detection Tools—0
Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification—0
Generating a Gold Standard for a Swedish Sentiment Lexicon—0
Generating artificial texts as substitution or complement of training data—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
Generating Effective Ensembles for Sentiment Analysis—0
Challenges for Open-domain Targeted Sentiment Analysis—0
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
GraPAT: a Tool for Graph Annotations—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