Sentiment Analysis
1297 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
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination
ChatGPT shows potential in analyzing public opinions on HPV vaccination using social media content.
Finding fake reviews in e-commerce platforms by using hybrid algorithms
Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data.
All in One: An Empirical Study of GPT for Few-Shot Aspect-Based Sentiment Anlaysis
In this study, we used GPTs for all sub-tasks of few-shot ABSA while defining a general learning paradigm for this application.
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts.
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods
In various real-world applications such as machine translation, sentiment analysis, and question answering, a pivotal role is played by NLP models, facilitating efficient communication and decision-making processes in domains ranging from healthcare to finance.
EFSA: Towards Event-Level Financial Sentiment Analysis
In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text.
TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
Motivated by these insights, we introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the predominant role of the text modality in MSA.
Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts
After the launch of ChatGPT v. 4 there has been a global vivid discussion on the ability of this artificial intelligence powered platform and some other similar ones for the automatic production of all kinds of texts, including scientific and technical texts.
Enhancing the Performance of Aspect-Based Sentiment Analysis Systems
Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity.
The Impact of Unstated Norms in Bias Analysis of Language Models
This approach is widely used in bias quantification.