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 3801–3850 of 5630 papers

TitleStatusHype
LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey—0
LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets—0
Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression—0
Local Interpretations for Explainable Natural Language Processing: A Survey—0
Locally Aggregated Feature Attribution on Natural Language Model Understanding—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks—0
Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using LLMs—0
Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications—0
Longitudinal Sentiment Classification of Reddit Posts—0
Lost in Translations? Building Sentiment Lexicons using Context Based Machine Translation—0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision—0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches—0
LSE au DEFT 2018 : Classification de tweets bas\'ee sur les r\'eseaux de neurones profonds (LSE at DEFT 2018 : Sentiment analysis model based on deep learning)—0
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction—0
LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification—0
Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis—0
Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter—0
LSTM based models stability in the context of Sentiment Analysis for social media—0
LSTM-based QoE Evaluation for Web Microservices' Reputation Scoring—0
LSTM Based Sentiment Analysis for Cryptocurrency Prediction—0
LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis—0
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets—0
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text—0
LT3: Sentiment Analysis of Figurative Tweets: piece of cake \#NotReally—0
LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set—0
[LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter—0
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing—0
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification—0
LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English—0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language—0
Machine Learning Algorithms for Depression Detection and Their Comparison—0
Machine Learning based English Sentiment Analysis—0
Machine Learning-Based Model for Sentiment and Sarcasm Detection—0
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums—0
Machine Learning for Food Review and Recommendation—0
Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago—0
Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter—0
Machine Learning Sentiment Prediction based on Hybrid Document Representation—0
Machine Translation for Accessible Multi-Language Text Analysis—0
Machine Translation for Machines: the Sentiment Classification Use Case—0
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness—0
MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis—0
MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations—0
MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification—0
mahaNLP: A Marathi Natural Language Processing Library—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis—0
Making Sense Of Distributed Representations With Activation Spectroscopy—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