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 2201–2250 of 5630 papers

TitleStatusHype
Audience Segmentation in Social Media—0
DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer—0
DEFT2018 : recherche d'information et analyse de sentiments dans des tweets concernant les transports en \^Ile de France (DEFT2018 : Information Retrieval and Sentiment Analysis in Tweets about Public Transportation in \^Ile de France Region )—0
A Type-Driven Tensor-Based Semantics for CCG—0
Anaphora and Coreference Resolution: A Review—0
Defending Multimodal Fusion Models against Single-Source Adversaries—0
A Two-Stage Classifier for Sentiment Analysis—0
An Annotation Framework for Luxembourgish Sentiment Analysis—0
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions—0
Deep Sequence Models for Text Classification Tasks—0
A Two-level Classifier for Discriminating Similar Languages—0
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models—0
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach—0
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis—0
An annotated corpus of quoted opinions in news articles—0
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications—0
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets—0
Deep Recursive Neural Networks for Compositionality in Language—0
AttitudeMiner: Mining Attitude from Online Discussions—0
An Annotated Corpus for Sentiment Analysis in Political News—0
Adversarial Attacks and Defense on Texts: A Survey—0
A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks—0
Abstractive Summarization of Product Reviews Using Discourse Structure—0
Ontology of Belief Diversity: A Community-Based Epistemological Approach—0
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis—0
Twitter Sentiment Analysis using Distributed Word and Sentence Representation—0
DeepNL: a Deep Learning NLP pipeline—0
Deep Neural Networks with Massive Learned Knowledge—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Deep neural network-based classification model for Sentiment Analysis—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Attention Modeling for Targeted Sentiment—0
An Analysis of Radicals-based Features in Subjectivity Classification on Simplified Chinese Sentences—0
Adversarial Attack on Sentiment Classification—0
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
Deep Markov Neural Network for Sequential Data Classification—0
Attention is Not Always What You Need: Towards Efficient Classification of Domain-Specific Text—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus—0
Deep Learning Training Procedure Augmentations—0
Attention-Enhancing Backdoor Attacks Against BERT-based Models—0
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings—0
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets—0
Analyzing users' sentiment towards popular consumer industries and brands on Twitter—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