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 2101–2150 of 5630 papers

TitleStatusHype
A Survey of Diffusion Models in Natural Language Processing—0
Automatically Inferring Implicit Properties in Similes—0
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis—0
A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions—0
Differentiable Window for Dynamic Local Attention—0
Different Contexts Lead to Different Word Embeddings—0
DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter—0
Automatically Constructing a Normalisation Dictionary for Microblogs—0
An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction—0
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons—0
Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions—0
Automatically Building a Corpus for Sentiment Analysis on Indonesian Tweets—0
Dialog speech sentiment classification for imbalanced datasets—0
Automatically augmenting an emotion dataset improves classification using audio—0
An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts—0
Development of a WAZOBIA-Named Entity Recognition System—0
Automatically Annotating A Five-Billion-Word Corpus of Japanese Blogs for Affect and Sentiment Analysis—0
Development of a General Purpose Sentiment Lexicon for Igbo Language—0
Developing the Bangla RST Discourse Treebank—0
Automatic Aggregation by Joint Modeling of Aspects and Values—0
Developing Language Resources and NLP Tools for the North Korean Language—0
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models—0
Automated Testing and Improvement of Named Entity Recognition Systems—0
Developing a concept-level knowledge base for sentiment analysis in Singlish—0
Determining sentiment in citation text and analyzing its impact on the proposed ranking index—0
Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams—0
An Empirical Examination of Online Restaurant Reviews—0
Adversarial Multiple Source Domain Adaptation—0
A Comprehensive Survey on Aspect Based Sentiment Analysis—0
A Calibration Method for Evaluation of Sentiment Analysis—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Determing Trustworthiness in E-Commerce Customer Reviews—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering—0
Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features—0
Detection of Implicit Citations for Sentiment Detection—0
Automated Classification of Text Sentiment—0
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow—0
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT—0
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs—0
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis—0
Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments—0
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models—0
Detecting Turnarounds in Sentiment Analysis: Thwarting—0
Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning—0
Auto-Generating Earnings Report Analysis via a Financial-Augmented LLM—0
An Effort to Measure Customer Relationship Performance in Indonesia's Fintech Industry—0
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment—0
Detecting speculations, contrasts and conditionals in consumer reviews—0
Detecting Sarcasm Using Different Forms Of Incongruity—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