Sentiment Analysis
1293 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
- Fine-Grained Opinion Analysis
- Aspect-Sentiment-Opinion Triplet Extraction
- Aspect-Category-Opinion-Sentiment Quadruple Extraction
- Vietnamese Aspect-Based Sentiment Analysis
- Vietnamese Sentiment Analysis
- Pcl Detection
Latest papers with no code
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.
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction.
Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
Scientific articles play a crucial role in advancing knowledge and informing research directions.
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets
In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention.
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors
In this paper, we propose Nested Product of Experts(NPoE) defense framework, which involves a mixture of experts (MoE) as a trigger-only ensemble within the PoE defense framework to simultaneously defend against multiple trigger types.