Sentiment Analysis
1305 papers with code • 39 benchmarks • 93 datasets
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:
Libraries
Use these libraries to find Sentiment Analysis models and implementationsDatasets
Subtasks
- Aspect-Based Sentiment Analysis (ABSA)
- Multimodal Sentiment Analysis
- Aspect Sentiment Triplet Extraction
- Twitter Sentiment Analysis
- Twitter Sentiment Analysis
- Aspect Term Extraction and Sentiment Classification
- target-oriented opinion words extraction
- Arabic Sentiment Analysis
- Persian Sentiment Analysis
- Aspect-oriented Opinion Extraction
- Aspect-Category-Opinion-Sentiment Quadruple Extraction
- Fine-Grained Opinion Analysis
- Aspect-Sentiment-Opinion Triplet Extraction
- Vietnamese Aspect-Based Sentiment Analysis
- Vietnamese Sentiment Analysis
- Pcl Detection
Latest papers with no code
The Call for Socially Aware Language Technologies
While NLP is getting better at solving the formal linguistic aspects, limited progress has been made in adding the social awareness required for language applications to work in all situations for all users.
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference.
Aspect and Opinion Term Extraction Using Graph Attention Network
We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features.
It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Comments
Sentiment analysis is an important tool for aggregating patient voices, in order to provide targeted improvements in healthcare services.
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin
We test the effect of this data augmentation on two critical NLP tasks: machine translation and sentiment analysis.
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis
Financial sentiment analysis allows financial institutions like Banks and Insurance Companies to better manage the credit scoring of their customers in a better way.
Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model.
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Specifically, we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics.
Investigating the dissemination of STEM content on social media with computational tools
Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious.
Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions.