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 4151–4200 of 5630 papers

TitleStatusHype
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets—0
NTNU: Domain Semi-Independent Short Message Sentiment Classification—0
NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis—0
NTOUA at IJCNLP-2017 Task 2: Predicting Sentiment Scores of Chinese Words and Phrases—0
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models—0
ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information—0
NUIG-Shubhanker@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet—0
NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis—0
Objective Assessment of Subjective Tasks in Crowdsourcing Applications—0
Occam's Gates—0
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin.—0
Offensive Language Detection Using Brown Clustering—0
oIQa: An Opinion Influence Oriented Question Answering Framework with Applications to Marketing Domain—0
OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields—0
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model—0
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning—0
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space—0
On-Demand Distributional Semantic Distance and Paraphrasing—0
ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects—0
One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations—0
On Gobbledygook and Mood of the Philippine Senate: An Exploratory Study on the Readability and Sentiment of Selected Philippine Senators' Microposts—0
On Limitations of LLM as Annotator for Low Resource Languages—0
Online Active Learning for Cost Sensitive Domain Adaptation—0
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm—0
Online Limited Memory Neural-Linear Bandits—0
Online Optimization Methods for the Quantification Problem—0
Only text? only image? or both? Predicting sentiment of internet memes—0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
On predictability of rare events leveraging social media: a machine learning perspective—0
On Prompt Sensitivity of ChatGPT in Affective Computing—0
On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?—0
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter—0
On the Automatic Learning of Sentiment Lexicons—0
On the ``Calligraphy'' of Books—0
On the Challenges of Sentiment Analysis for Dynamic Events—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis—0
On the Distribution of Lexical Features at Multiple Levels of Analysis—0
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
On the Effect of Word Order on Cross-lingual Sentiment Analysis—0
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations—0
On the Impact of Sentiment and Emotion Based Features in Detecting Online Sexual Predators—0
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets—0
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training—0
On the Robustness of Self-Attentive Models—0
On the Robustness of Sentiment Analysis for Stock Price Forecasting—0
On the Vector Representation of Utterances in Dialogue Context—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