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 2451–2500 of 5630 papers

TitleStatusHype
Cross-domain Sentiment Classification in Spanish—0
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
Cross-Domain Co-Extraction of Sentiment and Topic Lexicons—0
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction—0
An Algorithm for Routing Capsules in All Domains—0
A Comparison of Automatic Labelling Approaches for Sentiment Analysis—0
About Emotion Identification in Visual Sentiment Analysis—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Cross-discourse Development of Supervised Sentiment Analysis in the Clinical Domain—0
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-19—0
Assessing Sentiment Strength in Words Prior Polarities—0
An Advanced Press Review System Combining Deep News Analysis and Machine Learning Algorithms—0
CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language—0
Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification—0
Creative language explorations through a high-expressivity N-grams query language—0
Assessing Objective Recommendation Quality through Political Forecasting—0
An Account of Opinion Implicatures—0
Creation of Corpus and Analysis in Code-Mixed Kannada-English Social Media Data for POS Tagging—0
Creating emoji lexica from unsupervised sentiment analysis of their descriptions—0
Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis—0
Creating Domain Dependent Turkish WordNet and SentiNet—0
Creating a Sentiment Lexicon with Game-Specific Words for Analyzing NPC Dialogue in The Elder Scrolls V: Skyrim—0
A Multi-View Sentiment Corpus—0
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds—0
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi—0
Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks—0
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation—0
CPH: Sentiment analysis of Figurative Language on Twitter \#easypeasy \#not—0
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language—0
Adjective Intensity and Sentiment Analysis—0
A Comparison of Approaches for Sentiment Classification on Lithuanian Internet Comments—0
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
COVID-19 sentiment analysis via deep learning during the rise of novel cases—0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification—0
Covid-19 Public Sentiment Analysis for Indian Tweets Classification—0
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining—0
COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations—0
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network—0
A Distant Supervision Approach to Semantic Role Labeling—0
COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19—0
Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics—0
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining—0
A multi-task learning network using shared BERT models for aspect-based 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