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 3851–3900 of 5630 papers

TitleStatusHype
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM—0
Twitter Speaks: A Case of National Disaster Situational Awareness—0
Twitter Universal Dependency Parsing for African-American and Mainstream American English—0
Two-dimensional Sentiment Analysis of text—0
Two Stages Approach for Tweet Engagement Prediction—0
Two-Step Model for Sentiment Lexicon Extraction from Twitter Streams—0
Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets—0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification—0
Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems—0
Types of Aspect Terms in Aspect-Oriented Sentiment Labeling—0
UAIC at SemEval-2019 Task 3: Extracting Much from Little—0
UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis—0
UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis—0
UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network—0
UCM-I: A Rule-based Syntactic Approach for Resolving the Scope of Negation—0
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter—0
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation—0
UDLAP: Sentiment Analysis Using a Graph-Based Representation—0
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification—0
\'UFAL: Using Hand-crafted Rules in Aspect Based Sentiment Analysis on Parsed Data—0
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model—0
UFRGS: Identifying Categories and Targets in Customer Reviews—0
UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification—0
UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015—0
UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models—0
UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data—0
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text—0
UMCC\_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis—0
UMCC\_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter—0
UMCC\_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity—0
UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis—0
UMichigan: A Conditional Random Field Model for Resolving the Scope of Negation—0
Umigon: sentiment analysis for tweets based on terms lists and heuristics—0
UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition—0
Um novo corpo e os seus desafios (A new corpus and the challenges it offers)—0
High Risk of Political Bias in Black Box Emotion Inference Models—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Understanding and Improving Information Transfer in Multi-Task Learning—0
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study—0
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories—0
Understanding Neural Networks through Representation Erasure—0
Understanding Opinions Towards Climate Change on Social Media—0
Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers—0
Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models—0
Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50—0
Understanding Sarcoidosis Using Large Language Models and Social Media Data—0
Undivided Attention: Are Intermediate Layers Necessary for BERT?—0
UNIBA: Sentiment Analysis of English Tweets Combining Micro-blogging, Lexicon and Semantic Features—0
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective—0
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning—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