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 3151–3200 of 5630 papers

TitleStatusHype
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media—0
Forecasting Crude Oil Price Using Event Extraction—0
Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data—0
Forecasting with Economic News—0
Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script—0
Forward and Backward Knowledge Transfer for Sentiment Classification—0
Foundation Model's Embedded Representations May Detect Distribution Shift—0
Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models—0
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter—0
From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models—0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset—0
From Image to Text in Sentiment Analysis via Regression and Deep Learning—0
From newspaper to microblogging: What does it take to find opinions?—0
From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales—0
From Review to Rating: Exploring Dependency Measures for Text Classification—0
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation—0
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews—0
From Words and Exercises to Wellness: Farsi Chatbot for Self-Attachment Technique—0
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply—0
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos—0
Fusing location and text features for sentiment classification—0
Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences—0
Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling—0
Gaining and Losing Influence in Online Conversation—0
Gated Convolutional Neural Networks for Domain Adaptation—0
Gated Mechanism for Attention Based Multimodal Sentiment Analysis—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
Gender Prediction for Chinese Social Media Data—0
Gender stereotypes in the mediated personalization of politics: Empirical evidence from a lexical, syntactic and sentiment analysis—0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
Generalizable Multi-linear Attention Network—0
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction—0
Generalized Character-Level Spelling Error Correction—0
Generalized Sentiment-Bearing Expression Features for Sentiment Analysis—0
General Purpose Textual Sentiment Analysis and Emotion Detection Tools—0
Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification—0
Generating a Gold Standard for a Swedish Sentiment Lexicon—0
Generating artificial texts as substitution or complement of training data—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Generating Effective Ensembles for Sentiment Analysis—0
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models—0
Generating Polarity Lexicons with WordNet propagation in 5 languages—0
Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test—0
Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach—0
Generating Word and Document Embeddings for Sentiment Analysis—0
Generative Adversarial Imitation Learning for Empathy-based AI—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