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 2651–2700 of 5630 papers

TitleStatusHype
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals—0
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density—0
Corpora Preparation and Stopword List Generation for Arabic data in Social Network—0
CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets—0
A Joint Segmentation and Classification Framework for Sentiment Analysis—0
Extractive Summarization by Aggregating Multiple Similarities—0
Improving Document-Level Sentiment Classification Using Importance of Sentences—0
Extraction of Russian Sentiment Lexicon for Product Meta-Domain—0
Cell-aware Stacked LSTMs for Modeling Sentences—0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories—0
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration—0
Corpus based Amharic sentiment lexicon generation—0
Improving Formality Style Transfer with Context-Aware Rule Injection—0
Corpus-based discovery of semantic intensity scales—0
Corpus Creation for Sentiment Analysis in Code-Mixed Tulu Text—0
Extracting word lists for domain-specific implicit opinions from corpora—0
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach—0
Improving Minor Opinion Polarity Classification with Named Entity Analysis (L'apport des Entit\'es Nomm\'ees pour la classification des opinions minoritaires) [in French]—0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation—0
Correlating Facts and Social Media Trends on Environmental Quantities Leveraging Commonsense Reasoning and Human Sentiments—0
Improving Multimodal fusion via Mutual Dependency Maximisation—0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis—0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Extracting Emotion Phrases from Tweets using BART—0
Improving Sentiment Analysis in Arabic Using Word Representation—0
Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data—0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Improving Sentiment Analysis with Biofeedback Data—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling—0
Causing Emotion in Collocation:An Exploratory Data Analysis—0
Extracting Definitions and Hypernym Relations relying on Syntactic Dependencies and Support Vector Machines—0
Extracting Aspect Specific Opinion Expressions—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
A Joint Model for Chinese Microblog Sentiment Analysis—0
Improving Twitter Named Entity Recognition using Word Representations—0
Extracting Aspects Hierarchies using Rhetorical Structure Theory—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
Extracting Aspects and Polarity from Patents—0
Causality between Sentiment and Cryptocurrency Prices—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