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:
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
- Persian Sentiment Analysis
- Arabic 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
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).
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.
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons
We show that conditioning on bilingual lexicons is the key component of LexC-Gen. LexC-Gen is also practical -- it only needs a single GPU to generate data at scale.
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
This paper presents the Extensible Multi-Granularity Fusion (EMGF) network, which integrates information from dependency and constituent syntactic, attention semantic , and external knowledge graphs.
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble
We ask the following question in this study: are 01 loss sign activation neural networks hard to deceive with a popular black box text adversarial attack program called TextFooler?
Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
It implements an interpretability technique to analyze the contribution of textual and visual features during the generation of uncontrolled and controlled feedback.
EmojiCrypt: Prompt Encryption for Secure Communication with Large Language Models
While these models offer substantial benefits in terms of accessibility and functionality, they also introduce significant privacy concerns: the transmission and storage of user data in cloud infrastructures pose substantial risks of data breaches and unauthorized access to sensitive information; even if the transmission and storage of data is encrypted, the LLM service provider itself still knows the real contents of the data, preventing individuals or entities from confidently using such LLM services.
An Information-Theoretic Approach to Analyze NLP Classification Tasks
This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks.