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 2501–2550 of 5630 papers

TitleStatusHype
A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset—0
A Boosting-based Algorithm for Classification of Semi-Structured Text using the Frequency of Substructures—0
Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling—0
Cost and Benefit of Using WordNet Senses for Sentiment Analysis—0
Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Scores—0
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Correlation-Based Method for Sentiment Classification—0
Correlating Facts and Social Media Trends on Environmental Quantities Leveraging Commonsense Reasoning and Human Sentiments—0
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network—0
Corpus Statistics in Text Classification of Online Data—0
Corpus Creation for Sentiment Analysis in Code-Mixed Tulu Text—0
Corpus-based discovery of semantic intensity scales—0
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning—0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction—0
Corpus based Amharic sentiment lexicon generation—0
Corpus Based Amharic Sentiment Lexicon Generation—0
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network—0
Corporate Bankruptcy Prediction with Domain-Adapted BERT—0
Corporate Bankruptcy Prediction with BERT Model—0
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree—0
A Multi-task Approach to Predict Likability of Books—0
A Dictionary-Based Approach to Identifying Aspects Implied by Adjectives for Opinion Mining—0
Corpora Preparation and Stopword List Generation for Arabic data in Social Network—0
#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19—0
Aspect-Level Sentiment Analysis in Czech—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
Co-regularization Based Semi-supervised Domain Adaptation—0
Knowledge Graph Enhanced Aspect-Level Sentiment Analysis—0
A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus—0
Co-Regression for Cross-Language Review Rating Prediction—0
Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification—0
A Dependency Parser for Tweets—0
Coooolll: A Deep Learning System for Twitter Sentiment Classification—0
Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis—0
Aspect Extraction with Automated Prior Knowledge Learning—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media—0
Aspect Extraction through Semi-Supervised Modeling—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews—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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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