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 3551–3600 of 5630 papers

TitleStatusHype
Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams—0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt—0
Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis—0
SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis—0
SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling—0
Syntactical Analysis of the Weaknesses of Sentiment Analyzers—0
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction—0
Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration—0
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks—0
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification—0
Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis—0
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis—0
Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects—0
Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis—0
Synthetic Data Generation with LLM for Improved Depression Prediction—0
Synthetic Text Generation for Sentiment Analysis—0
Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models—0
Systematic improvement of user engagement with academic titles using computational linguistics—0
Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media—0
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications—0
SystemT: Declarative Text Understanding for Enterprise—0
SZTE-NLP: Aspect level opinion mining exploiting syntactic cues—0
SZTE-NLP: Sentiment Detection on Twitter Messages—0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction—0
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
Taking Antonymy Mask off in Vector Space—0
Tamil Language Computing: the Present and the Future—0
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling—0
Target-based Sentiment Annotation in Chinese Financial News—0
Target-dependent twitter sentiment classification with rich automatic features—0
Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network—0
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM—0
Targeted Data Generation: Finding and Fixing Model Weaknesses—0
Targeted Distillation for Sentiment Analysis—0
Targeted Sentiment to Understand Student Comments—0
Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis—0
Target-Sensitive Memory Networks for Aspect Sentiment Classification—0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
Explainable Semantic Communication for Text Tasks—0
Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data—0
Task-Specific Pre-Training and Cross Lingual Transfer for Sentiment Analysis in Dravidian Code-Switched Languages—0
Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need?—0
T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT—0
TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis—0
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture—0
TDAM: a Topic-Dependent Attention Model for 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
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