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 3951–4000 of 5630 papers

TitleStatusHype
Quality-Efficiency Trade-offs in Machine Learning for Text Processing—0
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network—0
Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2Code0
Compressing Word Embeddings via Deep Compositional Code LearningCode0
Towards Lower Bounds on Number of Dimensions for Word Embeddings—0
Graph Based Sentiment Aggregation using ConceptNet Ontology—0
Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information—0
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision—0
Geographical Evaluation of Word Embeddings—0
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews—0
Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features—0
Implicit Syntactic Features for Target-dependent Sentiment Analysis—0
Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification—0
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter—0
Toward Contextual Valence Shifters in Vietnamese Reviews—0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis—0
An Ensemble Method with Sentiment Features and Clustering Support—0
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—0
Multi-Domain Aspect Extraction Using Support Vector Machines—0
Ensemble Technique Utilization for Indonesian Dependency Parser—0
Cascading Multiway Attentions for Document-level Sentiment Classification—0
Automatic detection of stance towards vaccination in online discussion forums—0
Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis—0
Topic Based Sentiment Analysis Using Deep Learning—0
Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis—0
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis—0
Basic tasks of sentiment analysis—0
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
NoReC: The Norwegian Review CorpusCode0
Learning Phrase Embeddings from Paraphrases with GRUs—0
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for PredictionCode0
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis—0
On the Challenges of Sentiment Analysis for Dynamic Events—0
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion LexiconCode0
Semantic Sentiment Analysis of Twitter Data—0
Attentive Convolution: Equipping CNNs with RNN-style Attention MechanismsCode0
Wheel of Life: an initial investigation. Topic-Related Polarity Visualization in Personal Stories—0
Estudo explorat\'orio de categorias gramaticais com potencial de indicadores para a An\'alise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In Portuguese]—0
A Comparative Study for Sentiment Analysis on Election Brazilian News—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Investigating Opinion Mining through Language Varieties: a Case Study of Brazilian and European Portuguese tweets—0
A study on irony within the context of 7x1-PT corpus—0
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network—0
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models—0
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple DatasetsCode0
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision—0
EDEN: Evolutionary Deep Networks for Efficient Machine Learning—0
Using objective words in the reviews to improve the colloquial arabic sentiment analysis—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