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 3651–3700 of 5630 papers

TitleStatusHype
The Challenges of Multi-dimensional Sentiment Analysis Across Languages—0
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings—0
The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases—0
The Content Types Dataset: a New Resource to Explore Semantic and Functional Characteristics of Texts—0
The CUHK Discourse TreeBank for Chinese: Annotating Explicit Discourse Connectives for the Chinese TreeBank—0
The Dangerous Myth of the Star System—0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
The Effect of Gender and Age Differences on the Recognition of Emotions from Facial Expressions—0
The Effect of Round-Trip Translation on Fairness in Sentiment Analysis—0
The Effect of Temporal-based Term Selection for Text Classification—0
The Ellogon Pattern Engine: Context-free Grammars over Annotations—0
The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation—0
The Empirical Impact of Data Sanitization on Language Models—0
The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization—0
The French Social Media Bank: a Treebank of Noisy User Generated Content—0
The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
The IDC System for Sentiment Classification and Sarcasm Detection in Arabic—0
The Impact of Figurative Language on Sentiment Analysis—0
The Impact of Generative AI on Student Churn and the Future of Formal Education—0
The Impact of Indirect Machine Translation on Sentiment Classification—0
The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets—0
The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach—0
The Impact of Z\_score on Twitter Sentiment Analysis—0
The Importance of Calibration for Estimating Proportions from Annotations—0
The Inside-Outside Recursive Neural Network model for Dependency Parsing—0
The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection—0
The Language of Weather: Social Media Reactions to Weather Accounting for Climatic and Linguistic Baselines—0
The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis—0
The mathematics of language learning—0
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models—0
The Moral Foundations Weibo Corpus—0
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements—0
The New Eye of Government: Citizen Sentiment Analysis in Social Media—0
The Norwegian Dependency Treebank—0
The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses—0
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings—0
The perfect solution for detecting sarcasm in tweets \#not—0
The Political Speech Corpus of Bulgarian—0
The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection—0
The Rating Game: Sentiment Rating Reproducibility from Text—0
The Recent Advances in Automatic Term Extraction: A survey—0
The Role of Adverbs in Sentiment Analysis—0
The Role of Deep Learning in Financial Asset Management: A Systematic Review—0
The Role of Diversity in In-Context Learning for Large Language Models—0
The Role of Emotional Stability in Twitter Conversations—0
The Role of Emotions in Native Language Identification—0
The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter—0
The R package sentometrics to compute, aggregate and predict with textual sentiment—0
The Scope and Focus of Negation: A Complete Annotation Framework for Italian—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