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 3801–3850 of 5630 papers

TitleStatusHype
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only—0
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects—0
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis—0
Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets—0
Transfer Learning for Sequences via Learning to Collocate—0
Transfer Learning with Dynamic Distribution Adaptation—0
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya—0
Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis—0
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis—0
Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text—0
Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach—0
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
Translating Dialectal Arabic as Low Resource Language using Word Embedding—0
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis—0
TransRev: Modeling Reviews as Translations from Users to Items—0
TRBLLmaker -- Transformer Reads Between Lyrics Lines maker—0
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers—0
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media—0
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth—0
Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification—0
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness—0
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space—0
Try This Instead: Personalized and Interpretable Substitute Recommendation—0
TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis—0
TUGAS: Exploiting unlabelled data for Twitter sentiment analysis—0
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
Turkish Sentiment Analysis Using Machine Learning Methods: Application on Online Food Order Site Reviews—0
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer—0
TURKSENT: A Sentiment Annotation Tool for Social Media—0
TVD: A Reproducible and Multiply Aligned TV Series Dataset—0
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation—0
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter—0
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks—0
Tweeting and Being Ironic in the Debate about a Political Reform: the French Annotated Corpus TWitter-MariagePourTous—0
TweetNorm\_es: an annotated corpus for Spanish microtext normalization—0
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques—0
TwiInsight: Discovering Topics and Sentiments from Social Media Datasets—0
TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier—0
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification—0
Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction—0
Twitter Data Analysis: Izmir Earthquake Case—0
Twitter Dataset on the Russo-Ukrainian War—0
Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text—0
TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis—0
Twitter Language Identification Of Similar Languages And Dialects Without Ground Truth—0
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon—0
Twitter Sentiment Analysis—0
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination—0
Twitter Sentiment Analysis of Covid Vacciness—0
Twitter Sentiment Analysis System—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