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 4401–4450 of 5630 papers

TitleStatusHype
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification—0
Word-Level Language Identification and Predicting Codeswitching Points in Swahili-English Language Data—0
Does `well-being' translate on Twitter?—0
Deep Neural Networks with Massive Learned Knowledge—0
Citation Analysis with Neural Attention Models—0
Steps Toward Automatic Understanding of the Function of Affective Language in Support Groups—0
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network—0
Towards Broad-coverage Meaning Representation: The Case of Comparison Structures—0
Robust Gram EmbeddingsCode0
Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Context-Sensitive Lexicon Features for Neural Sentiment AnalysisCode0
Neural Sentiment Classification with User and Product AttentionCode0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population—0
How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles—0
Attention-based LSTM Network for Cross-Lingual Sentiment Classification—0
Convolutional Neural Network Language ModelsCode0
Automatic Extraction of Implicit Interpretations from Modal Constructions—0
Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
Mining Social Media for Open Innovation in Transportation Systems—0
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier—0
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksCode0
Lexicon Integrated CNN Models with Attention for Sentiment Analysis—0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods—0
Supervised Term Weighting Metrics for Sentiment Analysis in Short Text—0
Correlation-Based Method for Sentiment Classification—0
Neural Structural Correspondence Learning for Domain AdaptationCode0
Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models—0
Computing Sentiment Scores of Verb Phrases for Vietnamese—0
Sarcasm Detection in Chinese Using a Crowdsourced Corpus—0
Sentiment Clustering with Topic and Temporal Information from Large Email Dataset—0
Event Based Emotion Classification for News Articles—0
emoji2vec: Learning Emoji Representations from their DescriptionCode0
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon—0
Select-Additive Learning: Improving Generalization in Multimodal Sentiment AnalysisCode0
A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis—0
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification—0
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis—0
Sentiment Classification of Food Reviews—0
Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu—0
Sarcastic Soulmates: Intimacy and irony markers in social media messaging—0
Automatic Generation of Student Report Cards—0
Evaluative Pattern Extraction for Automated Text Generation—0
Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election—0
Applying Naive Bayes Classification to Google Play Apps Categorization—0
Semantic descriptions of 24 evaluational adjectives, for application in 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