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

Showing 48014850 of 5630 papers

TitleStatusHype
JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis0
Monotone Submodularity in Opinion SummariesCode0
The Rating Game: Sentiment Rating Reproducibility from Text0
Simple Text Mining for Sentiment Analysis of Political Figure Using Naive Bayes Classifier Method0
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment PredictionCode0
Molding CNNs for text: non-linear, non-consecutive convolutionsCode0
Enabling Complex Wikipedia Queries - Technical Report0
On Gobbledygook and Mood of the Philippine Senate: An Exploratory Study on the Readability and Sentiment of Selected Philippine Senators' Microposts0
Class Vectors: Embedding representation of Document Classes0
Document Embedding with Paragraph VectorsCode0
Target-dependent twitter sentiment classification with rich automatic features0
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination0
Topic-Based Chinese Message Polarity Classification System at SIGHAN8-Task20
Sentiment-Aspect Extraction based on Restricted Boltzmann Machines0
Sentiment and Belief: How to Think about, Represent, and Annotate Private States0
Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Scores0
User Based Aggregation for Biterm Topic Model0
The Web as an Implicit Training Set: Application to Noun Compounds Syntax and Semantics0
NDMSCS: A Topic-Based Chinese Microblog Polarity Classification System0
Finding Opinion Manipulation Trolls in News Community Forums0
Deep Learning Models for Sentiment Analysis in Arabic0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction0
ACBiMA: Advanced Chinese Bi-Character Word Morphological Analyzer0
A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets0
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network0
Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes0
Semi-Stacking for Semi-supervised Sentiment Classification0
Bekli:A Simple Approach to Twitter Text Normalization.0
Model Adaptation for Personalized Opinion Analysis0
Improving Twitter Named Entity Recognition using Word Representations0
Towards POS Tagging for Arabic Tweets0
Learning Cross-lingual Word Embeddings via Matrix Co-factorization0
Predicting Valence-Arousal Ratings of Words Using a Weighted Graph Method0
NCSU\_SAS\_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization0
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification0
A Joint Model for Chinese Microblog Sentiment Analysis0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis0
CT-SPA: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon0
Deep Markov Neural Network for Sequential Data Classification0
Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System0
Towards Debugging Sentiment Lexicons0
Rule-Based Weibo Messages Sentiment Polarity Classification towards Given Topics0
The Users Who Say `Ni': Audience Identification in Chinese-language Restaurant Reviews0
Stacked Ensembles of Information Extractors for Knowledge-Base Population0
Opinion Holder and Target Extraction based on the Induction of Verbal Categories0
Prior Polarity Lexical Resources for the Italian Language0
Overview of Topic-based Chinese Message Polarity Classification in SIGHAN 20150
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
Semantic Interpretation of Superlative Expressions via Structured Knowledge Bases0
A Convolution Kernel Approach to Identifying Comparisons in TextCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified