Sentiment Analysis
1289 papers with code • 43 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:
Benchmarks
These leaderboards are used to track progress in Sentiment Analysis
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Libraries
Use these libraries to find Sentiment Analysis models and implementationsDatasets
SST
IMDb Movie Reviews
MPQA Opinion Corpus
ReDial
TweetEval
Subtasks
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Aspect-Based Sentiment Analysis (ABSA)
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Multimodal Sentiment Analysis
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Aspect Sentiment Triplet Extraction
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Twitter Sentiment Analysis
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Twitter Sentiment Analysis
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Aspect Term Extraction and Sentiment Classification
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target-oriented opinion words extraction
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Persian Sentiment Analysis
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Arabic Sentiment Analysis
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Aspect-oriented Opinion Extraction
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Fine-Grained Opinion Analysis
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Aspect-Sentiment-Opinion Triplet Extraction
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Aspect-Category-Opinion-Sentiment Quadruple Extraction
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Vietnamese Aspect-Based Sentiment Analysis
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Vietnamese Sentiment Analysis
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Pcl Detection
Latest papers with no code
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis
In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA).
A Sentiment Analysis of Medical Text Based on Deep Learning
One of the research directions in text sentiment analysis is sentiment analysis of medical texts, which holds great potential for application in clinical diagnosis.
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space
To address the aforementioned problems, we propose a trustworthy multimodal sentiment ordinal network (TMSON) to improve performance in sentiment analysis.
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.
