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 751–800 of 5630 papers

TitleStatusHype
Benben: A Chinese Intelligent Conversational Robot—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Attention-based LSTM Network for Cross-Lingual Sentiment Classification—0
Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Analyzing the Impact of Sentiments of Scientific Articles on COVID-19 Vaccination Rates—0
Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification—0
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
Attention-Enhancing Backdoor Attacks Against BERT-based Models—0
Attention is Not Always What You Need: Towards Efficient Classification of Domain-Specific Text—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
Attention Modeling for Targeted Sentiment—0
ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research—0
Adversarial Attack on Sentiment Classification—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Bekli:A Simple Approach to Twitter Text Normalization.—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis—0
A Two-level Classifier for Discriminating Similar Languages—0
A Two-Stage Classifier for Sentiment Analysis—0
An Annotation Framework for Luxembourgish Sentiment Analysis—0
A Type-Driven Tensor-Based Semantics for CCG—0
Audience Segmentation in Social Media—0
Benchmarking Large Language Model Volatility—0
An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training—0
An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis—0
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis—0
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis—0
AUEB: Two Stage Sentiment Analysis of Social Network Messages—0
ATP: A holistic attention integrated approach to enhance ABSA—0
Analyzing Sentiment Word Relations with Affect, Judgment, and Appreciation—0
A Unified Framework for Structured Prediction: From Theory to Practice—0
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon—0
Analyzing Sentiment in Classical Chinese Poetry—0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
A user-centric model of voting intention from Social Media—0
Author Profiling at PAN: from Age and Gender Identification to Language Variety Identification (invited talk)—0
Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews—0
Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets—0
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities—0
AutoExtend: Combining Word Embeddings with Semantic Resources—0
Auto-Generating Earnings Report Analysis via a Financial-Augmented LLM—0
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models—0
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT—0
Automated Classification of Text Sentiment—0
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering—0
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis—0
Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams—0
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site—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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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