Popular GitHub Repositories for Aspect Categorization

? Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications

Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现。

Baidu's open-source Sentiment Analysis System.

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

Custom nodes for SDXL and SD1.5 including Multi-ControlNet, LoRA, Aspect Ratio, Process Switches, and many more nodes.

Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

code for our NAACL 2019 paper: "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis"

Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). 值得一读的方面级情感分析论文与相关资源集合

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)

all kinds of baseline models for long text classificaiton( text categorization)

Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

A Multi-Aspect Multi-Sentiment Dataset for aspect-based sentiment analysis.

My implementation of "Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution"

Official Implementation of "Multi-Head RAG: Solving Multi-Aspect Problems with LLMs"

Aspect Based Sentiment Analysis using End-to-End Memory Networks

Instructional learning for Aspect Based Sentiment Analysis [NAACL-2024]

Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF

[ACM MM 2018] Attribute-Aware Attention Model for Fine-grained Representation Learning

Keras Implementation of Aspect based Sentiment Analysis

A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).