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 4251–4300 of 5630 papers

TitleStatusHype
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Parameterized context windows in Random Indexing—0
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification—0
Paraphrasing with Large Language Models—0
ParlVote: A Corpus for Sentiment Analysis of Political Debates—0
Parser Adaptation for Social Media by Integrating Normalization—0
Parsing Morphologically Rich Languages: Introduction to the Special Issue—0
Parsing Russian: a hybrid approach—0
Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task—0
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction—0
Perceiving University Student's Opinions from Google App Reviews—0
Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets—0
Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis—0
Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models—0
Performance Investigation of Feature Selection Methods—0
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models—0
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method—0
Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification—0
Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis—0
Personality Driven Differences in Paraphrase Preference—0
Personality Trait Classification Using CNN-LSTM Model—0
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development—0
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning—0
Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter—0
Perturbation Sensitivity Analysis to Detect Unintended Model Biases—0
Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis—0
PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis—0
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning—0
PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis—0
PICS: Pipeline for Image Captioning and Search—0
Pivot Based Language Modeling for Improved Neural Domain Adaptation—0
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore—0
PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets—0
Plongements lexicaux sp\'ecifiques \`a la langue arabe : application \`a l'analyse d'opinions (Arabic-specific embedddings : application in Sentiment Analysis)—0
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation—0
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags—0
PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering—0
Polarity and Intensity: the Two Aspects of Sentiment Analysis—0
Polarity based Sarcasm Detection using Semigraph—0
Polarity Classification of Short Product Reviews via Multiple Cluster-based SVM Classifiers—0
Polarity Consistency Checking for Sentiment Dictionaries—0
Polarity detection movie reviews in hindi language—0
Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment—0
Polarization Measurement of High Dimensional Social Media Messages With Support Vector Machine Algorithm Using Mapreduce—0
PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation—0
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation—0
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish—0
PoliTa: A multitagger for Polish—0
POLITICAL-ADS: An annotated corpus for modeling event-level evaluativity—0
Political Ideology Detection Using Recursive Neural Networks—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