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 3151–3175 of 5630 papers

Title	Date	Tasks	Status	Hype
RoBERTa: A Robustly Optimized BERT Pretraining Approach	Jul 26, 2019	Common Sense ReasoningDocument Image Classification	CodeCode Available	1
SpanBERT: Improving Pre-training by Representing and Predicting Spans	Jul 24, 2019	Coreference ResolutionLinguistic Acceptability	CodeCode Available	0
Interpretable and Steerable Sequence Learning via Prototypes	Jul 23, 2019	Sentiment Analysis	CodeCode Available	0
SentiMATE: Learning to play Chess through Natural Language Processing	Jul 18, 2019	Decision MakingSentiment Analysis	—Unverified	0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches	Jul 17, 2019	BIG-bench Machine LearningGeneral Classification	—Unverified	0
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis	Jul 15, 2019	General ClassificationSentiment Analysis	—Unverified	0
A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning	Jul 15, 2019	Sentiment Analysis	—Unverified	0
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis	Jul 14, 2019	Sentiment AnalysisTransfer Learning	—Unverified	0
ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis	Jul 12, 2019	Sentiment Analysis	—Unverified	0
Neural Networks as Explicit Word-Based Rules	Jul 10, 2019	General ClassificationSentiment Analysis	—Unverified	0
Sentiment Analysis Challenges in Persian Language	Jul 9, 2019	ManagementRecommendation Systems	—Unverified	0
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition	Jul 6, 2019	Emotion RecognitionSentiment Analysis	—Unverified	0
A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic	Jul 5, 2019	Natural Language InferenceNegation	—Unverified	0
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering	Jul 4, 2019	Representation LearningSentiment Analysis	CodeCode Available	0
Deep neural network-based classification model for Sentiment Analysis	Jul 3, 2019	ClassificationGeneral Classification	—Unverified	0
Neural Image Captioning	Jul 2, 2019	Image CaptioningMachine Translation	—Unverified	0
Plongements lexicaux sp\'ecifiques \`a la langue arabe : application \`a l'analyse d'opinions (Arabic-specific embedddings : application in Sentiment Analysis)	Jul 1, 2019	Sentiment Analysis	—Unverified	0
Can Modern Standard Arabic Approaches be used for Arabic Dialects? Sentiment Analysis as a Case Study	Jul 1, 2019	Sentiment Analysis	—Unverified	0
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity	Jul 1, 2019	ClassificationDocument Embedding	CodeCode Available	0
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis	Jul 1, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Sentiment Analysis on Naija-Tweets	Jul 1, 2019	Sentiment Analysis	—Unverified	0
De-Mixing Sentiment from Code-Mixed Text	Jul 1, 2019	SentenceSentiment Analysis	—Unverified	0
Investigating Political Herd Mentality: A Community Sentiment Based Approach	Jul 1, 2019	General ClassificationSentiment Analysis	—Unverified	0
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation	Jul 1, 2019	Domain AdaptationLanguage Modeling	CodeCode Available	0
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network	Jul 1, 2019	General ClassificationQuestion Answering	—Unverified	0

Show:10 25 50

← PrevPage 127 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	MT-DNN-SMART	Accuracy	97.5	—	Unverified
3	T5-11B	Accuracy	97.5	—	Unverified
4	MUPPET Roberta Large	Accuracy	97.4	—	Unverified
5	T5-3B	Accuracy	97.4	—	Unverified
6	ALBERT	Accuracy	97.1	—	Unverified
7	StructBERTRoBERTa ensemble	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