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 5151–5200 of 5630 papers

TitleStatusHype
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation—0
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi—0
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds—0
Creating a Sentiment Lexicon with Game-Specific Words for Analyzing NPC Dialogue in The Elder Scrolls V: Skyrim—0
Creating Domain Dependent Turkish WordNet and SentiNet—0
Creating emoji lexica from unsupervised sentiment analysis of their descriptions—0
Creation of Corpus and Analysis in Code-Mixed Kannada-English Social Media Data for POS Tagging—0
Creative language explorations through a high-expressivity N-grams query language—0
Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification—0
CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language—0
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-19—0
Cross-discourse Development of Supervised Sentiment Analysis in the Clinical Domain—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Cross-Domain Co-Extraction of Sentiment and Topic Lexicons—0
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis—0
Cross-domain Sentiment Classification in Spanish—0
Cross-Domain Sentiment Classification using Vector Embedded Domain Representations—0
Cross-Domain Sentiment Classification with Target Domain Specific Information—0
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets—0
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic—0
Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic—0
Cross-lingual alignments of ELMo contextual embeddings—0
Cross-lingual Flames Detection in News Discussions—0
Cross-Lingual Image Caption Generation—0
Cross-Lingual Mixture Model for Sentiment Classification—0
Cross-Lingual News Event Correlation for Stock Market Trend Prediction—0
Cross-Lingual Sentiment Analysis for Indian Languages using Linked WordNets—0
Cross Lingual Sentiment Analysis using Modified BRAE—0
Cross-Lingual Sentiment Analysis Without (Good) Translation—0
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning—0
Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages—0
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis—0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning—0
Cross-Modality Gated Attention Fusion for Multimodal Sentiment Analysis—0
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition—0
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications—0
Crowd-Powered Data Mining—0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English—0
Crowdsourcing Annotation of Non-Local Semantic Roles—0
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification—0
CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market—0
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons—0
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification—0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3—0
CT-SPA: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon—0
CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text—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