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 4051–4100 of 5630 papers

TitleStatusHype
Multi-Source Hard and Soft Information Fusion Approach for Accurate Cryptocurrency Price Movement Prediction—0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification—0
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis—0
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment—0
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis—0
Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—0
Multi-task memory networks for category-specific aspect and opinion terms co-extraction—0
Multi-task Prompt Words Learning for Social Media Content Generation—0
Multi-Task Stance Detection with Sentiment and Stance Lexicons—0
Multi-Zone Unit for Recurrent Neural Networks—0
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection—0
My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks—0
``My Curiosity was Satisfied, but not in a Good Way'': Predicting User Ratings for Online Recipes—0
Myers-Briggs personality classification from social media text using pre-trained language models—0
"My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguise—0
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning—0
Named-Entity Based Sentiment Analysis of Nepali News Media Texts—0
Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models—0
Narrowing the Loop: Integration of Resources and Linguistic Dataset Development with Interactive Machine Learning—0
Natural Language Processing for Dialects of a Language: A Survey—0
Natural Language Processing in Political Campaigns—0
Natural language processing on customer note data—0
Natural Language Processing, Sentiment Analysis and Clinical Analytics—0
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
NCSU\_SAS\_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization—0
NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases—0
NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis—0
NCYU at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations—0
NDMSCS: A Topic-Based Chinese Microblog Polarity Classification System—0
Negation and Modality in Machine Translation—0
Negation handling for Amharic sentiment classification—0
Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic—0
Negation Scope Detection for Twitter Sentiment Analysis—0
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.—0
NegPar: A parallel corpus annotated for negation—0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English—0
NEUDM: A System for Topic-Based Message Polarity Classification—0
Neural Attention Model for Classification of Sentences that Support Promoting/Suppressing Relationship—0
Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification—0
Neural Dependency Coding inspired Multimodal Fusion—0
Neural Image Captioning—0
Neural Metaphor Detecting with CNN-LSTM Model—0
Neural Monkey: The Current State and Beyond—0
Neural Natural Language Processing for Long Texts: A Survey on Classification and Summarization—0
GIM: Gaussian Isolation Machines—0
Neural Networks as Explicit Word-Based Rules—0
Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning—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