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 3301–3350 of 5630 papers

TitleStatusHype
Sentiment Analysis Using Collaborated Opinion Mining—0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities—0
Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks—0
Sentiment Analysis - What are we talking about?—0
Sentiment analysis with adaptive multi-head attention in Transformer—0
Sentiment Analysis with Contextual Embeddings and Self-Attention—0
Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data—0
Sentiment analysis with genetically evolved Gaussian kernels—0
Sentiment Analysis with R: Natural Language Processing for Semi-Automated Assessments of Qualitative Data—0
Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets—0
Sentiment and Belief: How to Think about, Represent, and Annotate Private States—0
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media—0
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis—0
Sentiment and Hashtag-aware Attentive Deep Neural Network for Multimodal Post Popularity Prediction—0
Sentiment and Sarcasm Classification with Multitask Learning—0
Sentiment-Aspect Extraction based on Restricted Boltzmann Machines—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Sentiment-Aware Recommendation System for Healthcare using Social Media—0
Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective—0
Sentiment-based Engagement Strategies for intuitive Human-Robot Interaction—0
Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies—0
Sentiment Classification for Movie Reviews in Chinese Using Parsing-based Methods—0
Sentiment Classification in Swahili Language Using Multilingual BERT—0
Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms—0
Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags—0
Sentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation—0
Sentiment Classification of Customer Reviews about Automobiles in Roman Urdu—0
Sentiment Classification of Food Reviews—0
Sentiment classification of online political discussions: a comparison of a word-based and dependency-based method—0
Sentiment Classification of Thai Central Bank Press Releases Using Supervised Learning—0
Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features—0
Sentiment Classification towards Question-Answering with Hierarchical Matching Network—0
Sentiment Classification using Images and Label Embeddings—0
Sentiment Classification using N-gram IDF and Automated Machine Learning—0
Sentiment Classification using Rough Set based Hybrid Feature Selection—0
Sentiment Classification via a Response Recalibration Framework—0
Sentiment Classification with Graph Co-Regularization—0
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network—0
Sentiment Clustering with Topic and Temporal Information from Large Email Dataset—0
Sentiment Domain Adaptation with Multiple Sources—0
Sentiment Expression Boundaries in Sentiment Polarity Classification—0
Sentiment Expression via Emoticons on Social Media—0
Sentiment Flow - A General Model of Web Review Argumentation—0
Sentiment Identification in Code-Mixed Social Media Text—0
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings—0
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis—0
Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks—0
Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association—0
Sentiment Lexicon Interpolation and Polarity Estimation of Objective and Out-Of-Vocabulary Words to Improve Sentiment Classification on Microblogging—0
Sentiment Lexicons for Arabic Social Media—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