Sentiment Analysis
1295 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
KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes
This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind.
LlamBERT: Large-scale low-cost data annotation in NLP
Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks.
Community Needs and Assets: A Computational Analysis of Community Conversations
To facilitate this task, we introduce the first dataset about community needs and assets consisting of 3, 511 conversations from Reddit, annotated using crowdsourced workers.
Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis
This necessitates a dual focus on both the syntactic information of individual utterances and the semantic interaction among them.
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis
While existing research narrowly focuses on single-domain applications constrained by methodological limitations and data scarcity, the reality is that sentiment naturally traverses multiple domains.
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review).
Gradient-Guided Modality Decoupling for Missing-Modality Robustness
In aid of this indicator, we present a novel Gradient-guided Modality Decoupling (GMD) method to decouple the dependency on dominating modalities.
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks.
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Exploring and Applying Audio-Based Sentiment Analysis in Music
Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text.