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 2601–2650 of 5630 papers

TitleStatusHype
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network—0
IITPatna: Supervised Approach for Sentiment Analysis in Twitter—0
Extractive Summarization by Aggregating Multiple Similarities—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
IITPSemEval: Sentiment Discovery from 140 Characters—0
IITP: Supervised Machine Learning for Aspect based Sentiment Analysis—0
IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis—0
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases—0
iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases—0
Imbalanced Sentiment Classification Enhanced with Discourse Marker—0
Immigration in the Manifestos and Parliament Speeches of Danish Left and Right Wing Parties between 2009 and 2020—0
Controlled CNN-based Sequence Labeling for Aspect Extraction—0
Impact of Corpus Diversity and Complexity on NER Performance—0
Impact of Feature Selection on Micro-Text Classification—0
Impact of Sentiment Analysis in Fake Review Detection—0
Impact of Stickers on Multimodal Chat Sentiment Analysis and Intent Recognition: A New Task, Dataset and Baseline—0
Impact of the COVID-19 outbreak on Italy's country reputation and stock market performance: a sentiment analysis approach—0
Impacts Towards a comprehensive assessment of the book impact by integrating multiple evaluation sources—0
Extraction of Russian Sentiment Lexicon for Product Meta-Domain—0
Implicit and Explicit Aspect Extraction in Financial Microblogs—0
Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words—0
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings—0
Cell-aware Stacked LSTMs for Modeling Sentences—0
Implicit Polarity and Implicit Aspect Recognition in Opinion Mining—0
Implicit Sentiment Analysis Based on Chain of Thought Prompting—0
Implicit Sentiment Analysis with Event-centered Text Representation—0
Extracting word lists for domain-specific implicit opinions from corpora—0
Importance of Self-Attention for Sentiment Analysis—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Aspect Extraction through Semi-Supervised Modeling—0
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach—0
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Improved Twitter Sentiment Prediction through Cluster-then-Predict Model—0
Improved Word Embeddings with Implicit Structure Information—0
Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis—0
Aspect Extraction with Automated Prior Knowledge Learning—0
Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM—0
Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features—0
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation—0
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation—0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla—0
Extracting Emotion Phrases from Tweets using BART—0
Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector—0
Improving Citation Polarity Classification with Product Reviews—0
Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization—0
Causing Emotion in Collocation:An Exploratory Data 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