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 1601–1650 of 5630 papers

TitleStatusHype
Active Information Acquisition—0
Accommodations in Tuscany as Linked Data—0
Beyond the Black Box: Interpretability of LLMs in Finance—0
Beyond Sentiment: The Manifold of Human Emotions—0
Beyond Sentiment: Leveraging Topic Metrics for Political Stance Classification—0
A novel approach to sentiment analysis in Persian using discourse and external semantic information—0
Beyond Multiword Expressions: Processing Idioms and Metaphors—0
Anotando um Corpus de Not\' para a An\'alise de Sentimentos: um Relato de Experi\^encia (Annotating a corpus of News for Sentiment Analysis: An Experience Report)—0
Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios—0
An opinion about opinions about opinions: subjectivity and the aggregate reader—0
A Generative Model for Identifying Target Companies of Microblogs—0
Better Queries for Aspect-Category Sentiment Classification—0
Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models—0
Better Document-level Sentiment Analysis from RST Discourse Parsing—0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis—0
Best Practices in the Creation and Use of Emotion Lexicons—0
Annotation Scheme for Constructing Sentiment Corpus in Korean—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus—0
A Generalised Hybrid Architecture for NLP—0
A Cross-Validation Study of Turkish Sentiment Analysis Datasets and Tools—0
A CCG-based Approach to Fine-Grained Sentiment Analysis—0
BERT or FastText? A Comparative Analysis of Contextual as well as Non-Contextual Embeddings—0
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights—0
Annotating Uncertainty in Hungarian Webtext—0
Annotating the Interaction between Focus and Modality: the case of exclusive particles—0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews—0
BERTer: The Efficient One—0
Annotating Sentiment and Irony in the Online Italian Political Debate on \#labuonascuola—0
A functional linguistic perspective on evaluation—0
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets—0
BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis—0
Annotating Opinions in German Political News—0
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability—0
Annotating Opinions and Opinion Targets in Student Course Feedback—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis—0
Annotating Modal Expressions in the Chinese Treebank—0
BERTaú: Itaú BERT for digital customer service—0
BERT at SemEval-2020 Task 8: Using BERT to Analyse Meme Emotions—0
Annotating Italian Social Media Texts in Universal Dependencies—0
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text—0
A COVID-19 news coverage mood map of Europe—0
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification—0
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search—0
BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier—0
Annotated Corpus for Sentiment Analysis in Odia Language—0
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP—0
Benefactive/Malefactive Event and Writer Attitude Annotation—0
An LSTM model for Twitter 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