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.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3301–3325 of 5630 papers

Title	Date	Tasks	Status	Hype
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks	Mar 28, 2019	Natural Language InferenceSentence	CodeCode Available	0
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification	Mar 27, 2019	ClassificationDomain Adaptation	—Unverified	0
Micro-expression detection in long videos using optical flow and recurrent neural networks	Mar 26, 2019	Micro-Expression SpottingOptical Flow Estimation	—Unverified	0
An end-to-end Neural Network Framework for Text Clustering	Mar 22, 2019	ClusteringRepresentation Learning	—Unverified	0
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence	Mar 22, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Affect in Tweets Using Experts Model	Mar 20, 2019	Mixture-of-Expertsmodel	—Unverified	0
Cloze-driven Pretraining of Self-attention Networks	Mar 19, 2019	Constituency ParsingNamed Entity Recognition (NER)	—Unverified	0
Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques	Mar 18, 2019	BIG-bench Machine LearningClassification	—Unverified	0
Effects of padding on LSTMs and CNNs	Mar 18, 2019	Sentiment Analysis	—Unverified	0
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only	Mar 14, 2019	Aspect ExtractionDescriptive	—Unverified	0
Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward	Mar 13, 2019	Sentiment Analysis	CodeCode Available	0
Offensive Language Analysis using Deep Learning Architecture	Mar 12, 2019	Abuse DetectionDeep Learning	CodeCode Available	0
Twitter Speaks: A Case of National Disaster Situational Awareness	Mar 7, 2019	ManagementSentiment Analysis	—Unverified	0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models	Mar 4, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis	Feb 28, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Induction Networks for Few-Shot Text Classification	Feb 27, 2019	Few-Shot Text ClassificationGeneral Classification	CodeCode Available	1
Star-Transformer	Feb 25, 2019	Named Entity Recognition (NER)Natural Language Inference	CodeCode Available	0
Cooperative Learning of Disjoint Syntax and Semantics	Feb 25, 2019	Domain GeneralizationNatural Language Inference	CodeCode Available	0
Attentional Encoder Network for Targeted Sentiment Classification	Feb 25, 2019	Aspect-Based Sentiment Analysis (ABSA)Classification	CodeCode Available	0
Transfer Learning for Sequences via Learning to Collocate	Feb 25, 2019	NERPOS	—Unverified	0
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications	Feb 23, 2019	BIG-bench Machine LearningDocument Classification	—Unverified	0
VCWE: Visual Character-Enhanced Word Embeddings	Feb 23, 2019	named-entity-recognitionNamed Entity Recognition	CodeCode Available	0
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation	Feb 23, 2019	Multi-Label Text ClassificationSentiment Analysis	CodeCode Available	0
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining	Feb 22, 2019	Opinion MiningSentiment Analysis	—Unverified	0
Stacking with Neural network for Cryptocurrency investment	Feb 21, 2019	Feature ImportanceSentiment Analysis	—Unverified	0

Show:10 25 50

← PrevPage 133 of 226Next →

All datasets SST-2 Binary classification IMDb SST-5 Fine-grained classification Yelp Binary classification MR Yelp Fine-grained classification BanglaBook DynaSent SST-3 Sentiment Merged User and product information Amazon Review Full

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Word+ES (Scratch)	Attack Success Rate	100	—	Unverified
2	T5-11B	Accuracy	97.5	—	Unverified
3	MT-DNN-SMART	Accuracy	97.5	—	Unverified
4	T5-3B	Accuracy	97.4	—	Unverified
5	MUPPET Roberta Large	Accuracy	97.4	—	Unverified
6	StructBERTRoBERTa ensemble	Accuracy	97.1	—	Unverified
7	ALBERT	Accuracy	97.1	—	Unverified
8	XLNet (single model)	Accuracy	97	—	Unverified
9	SMARTRoBERTa	Dev Accuracy	96.9	—	Unverified
10	ELECTRA	Accuracy	96.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-large with LlamBERT	Accuracy	96.68	—	Unverified
2	RoBERTa-large	Accuracy	96.54	—	Unverified
3	XLNet	Accuracy	96.21	—	Unverified
4	Heinsen Routing + RoBERTa Large	Accuracy	96.2	—	Unverified
5	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	96.1	—	Unverified
6	GraphStar	Accuracy	96	—	Unverified
7	DV-ngrams-cosine with NB sub-sampling + RoBERTa.base	Accuracy	95.94	—	Unverified
8	DV-ngrams-cosine + RoBERTa.base	Accuracy	95.92	—	Unverified
9	Roberta_Large ST + Cosine Similarity Loss	Accuracy	95.9	—	Unverified
10	BERT large finetune UDA	Accuracy	95.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Llama-3.3-70B + CAPO	Accuracy	62.27	—	Unverified
2	Mistral-Small-24B + CAPO	Accuracy	60.2	—	Unverified
3	Heinsen Routing + RoBERTa Large	Accuracy	59.8	—	Unverified
4	RoBERTa-large+Self-Explaining	Accuracy	59.1	—	Unverified
5	Qwen2.5-32B + CAPO	Accuracy	59.07	—	Unverified
6	Heinsen Routing + GPT-2	Accuracy	58.5	—	Unverified
7	BCN+Suffix BiLSTM-Tied+CoVe	Accuracy	56.2	—	Unverified
8	BERT Large	Accuracy	55.5	—	Unverified
9	LM-CPPF RoBERTa-base	Accuracy	54.9	—	Unverified
10	BCN+ELMo	Accuracy	54.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Char-level CNN	Error	4.88	—	Unverified
2	SVDCNN	Error	4.74	—	Unverified
3	LEAM	Error	4.69	—	Unverified
4	fastText, h=10, bigram	Error	4.3	—	Unverified
5	SWEM-hier	Error	4.19	—	Unverified
6	SRNN	Error	3.96	—	Unverified
7	M-ACNN	Error	3.89	—	Unverified
8	DNC+CUW	Error	3.6	—	Unverified
9	CCCapsNet	Error	3.52	—	Unverified
10	Block-sparse LSTM	Error	3.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Millions of Emoji	Training Time	1,500	—	Unverified
2	VLAWE	Accuracy	93.3	—	Unverified
3	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	92.5	—	Unverified
4	AnglE-LLaMA-7B	Accuracy	91.09	—	Unverified
5	byte mLSTM7	Accuracy	86.8	—	Unverified
6	MEAN	Accuracy	84.5	—	Unverified
7	RNN-Capsule	Accuracy	83.8	—	Unverified
8	Capsule-B	Accuracy	82.3	—	Unverified
9	SuBiLSTM-Tied	Accuracy	81.6	—	Unverified
10	USE_T+CNN	Accuracy	81.59	—	Unverified