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 3751–3800 of 5630 papers

TitleStatusHype
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework—0
Lex2Sent: A bagging approach to unsupervised sentiment analysis—0
Lexical Acquisition for Opinion Inference: A Sense-Level Lexicon of Benefactive and Malefactive Events—0
Lexical and Hierarchical Topic Regression—0
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification—0
Lexical Based Semantic Orientation of Online Customer Reviews and Blogs—0
Lexical Substitution for Evaluating Compositional Distributional Models—0
Lexicon based Fine-tuning of Multilingual Language Models for Sentiment Analysis of Low-resource Languages—0
Lexicon-based Methods vs. BERT for Text Sentiment Analysis—0
Lexicon-based Sentiment Analysis for Persian Text—0
Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models—0
Lexicon Guided Attentive Neural Network Model for Argument Mining—0
Lexicon Integrated CNN Models with Attention for Sentiment Analysis—0
LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification—0
LICD: A Language-Independent Approach for Aspect Category Detection—0
Lifelong Learning for Sentiment Classification—0
Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets—0
Life still goes on: Analysing Australian WW1 Diaries through Distant Reading—0
Lightweight Models for Multimodal Sequential Data—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
LIMSI\_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis—0
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation—0
Linear Projections of Teacher Embeddings for Few-Class Distillation—0
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis—0
Lingmotif: Sentiment Analysis for the Digital Humanities—0
Linguagrid: a network of Linguistic and Semantic Services for the Italian Language.—0
Linguistically Informed Tweet Categorization for Online Reputation Management—0
Linguistically motivated Language Resources for Sentiment Analysis—0
Linguistically Regularized LSTM for Sentiment Classification—0
Linguistically Regularized LSTMs for Sentiment Classification—0
Linguistic approach based Transfer Learning for Sentiment Classification in Hindi—0
Linguistic Complexity and Socio-cultural Patterns in Hip-Hop Lyrics—0
Linguistic Entity Masking to Improve Cross-Lingual Representation of Multilingual Language Models for Low-Resource Languages—0
Linguistic features for sentence difficulty prediction in ABSA—0
Linguistic Fingerprint in Transformer Models: How Language Variation Influences Parameter Selection in Irony Detection—0
Linguistic Linked Data for Sentiment Analysis—0
Linguistic Structured Sparsity in Text Categorization—0
Linguistic Understanding of Complaints and Praises in User Reviews—0
Lingusitic Analysis of Multi-Modal Recurrent Neural Networks—0
Linking microblogging sentiments to stock price movement: An application of GPT-4—0
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification—0
Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media—0
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination—0
LiSSS: A toy corpus of Spanish Literary Sentences for Emotions detection—0
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models—0
LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing—0
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction—0
LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms—0
LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma—0
LLMs in Political Science: Heralding a New Era of Visual Analysis—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