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 1851–1900 of 5630 papers

TitleStatusHype
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
A Web Scraping Methodology for Bypassing Twitter API Restrictions—0
A New Approach To Text Rating Classification Using Sentiment Analysis—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Embedding-based Approaches to Hyperpartisan News Detection—0
Does Size Matter? Text and Grammar Revision for Parsing Social Media Data—0
Embedding Learning Through Multilingual Concept Induction—0
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis—0
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models—0
On Commonsense Cues in BERT for Solving Commonsense Tasks—0
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
A context-based model for Sentiment Analysis in Twitter—0
Does BERT look at sentiment lexicon?—0
Does `well-being' translate on Twitter?—0
Emoji-based Co-attention Network for Microblog Sentiment Analysis—0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task—0
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision—0
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification—0
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels—0
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning—0
Document-level Sentiment Inference with Social, Faction, and Discourse Context—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training—0
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses—0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension—0
Emotional Analysis of Fashion Trends Using Social Media and AI: Sentiment Analysis on Twitter for Fashion Trend Forecasting—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts—0
A Vector Space Approach for Aspect Based Sentiment Analysis—0
Emotional Tendency Identification for Micro-blog Topics Based on Multiple Characteristics—0
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing—0
Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews—0
Do Convolutional Networks need to be Deep for Text Classification ?—0
Emotion Enriched Retrofitted Word Embeddings—0
AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis—0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models—0
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse—0
Emotion Recognition for Vietnamese Social Media Text—0
Emotions and NLP: Future Directions—0
Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning—0
Aesthetic Visual Question Answering of Photographs—0
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach—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