Consensus score for tripadvisor

Overview

ContripScore

ContripScore is essentially a score that combines an Internet platform rating and a consensus rating from sentiment analysis (For instance, by Nguyen et al. 2020). This novel scoring system has the advantage of bringing together both sources of information (sentiment analysis and overall rating) into a single score. Furthermore, as the consensus ranking of the sentiment analysis, our ContripScore allows differentiation of closely rated items while providing better interpretability. The increased interpretability arises from our ContripScore representing a value between 0 and 5 (without scaling. Figure below A and B) or 1 and 5 (with scaling. Figure C and D) . This range, related to any platform, could improve the ranking and the user's understanding of how good it is the experience they are buying.

alt text

Contents

Installation

Clone repository

First download the repository:

    git clone https://github.com/pepebonet/contripscore.git

Install dependencies

We highly recommend to use a virtual environment to run the scripts:

Create environment and install contripscore:

    conda create --name contripscore python=3.8
    conda activate contripscore
    pip install -r requirements.txt

Usage

Main

To compute the ContripScore of a given hotel, establishment or experience run the following command:

python scripts/main.py -tr 5.0 -cv 1.0 -w1 0.5 -w2 10 -o output_folder

Commands

-tr: Tripadvisor rating

-cv: Consensus value from NLP

-w1: Weight 1 for the score calculation

-w2: Weight 2 for the score calculation

Reproduce Figures

To obtain the figure run the following commands (First without scaling and the second with it):

python scripts/comparison_figures.py
python scripts/comparison_figures.py -sf
Owner
Pepe
PhD student working in the field of computational oncology
Pepe
Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization

Fishr: Invariant Gradient Variances for Out-of-distribution Generalization Official PyTorch implementation of the Fishr regularization for out-of-dist

62 Dec 22, 2022
Lipschitz-constrained Unsupervised Skill Discovery

Lipschitz-constrained Unsupervised Skill Discovery This repository is the official implementation of Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Hong

Seohong Park 17 Dec 18, 2022
Linear Variational State Space Filters

Linear Variational State Space Filters To set up the environment, use the provided scripts in the docker/ folder to build and run the codebase inside

0 Dec 13, 2021
A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms

Denoising Diffusion Probabilistic Model for Proteins Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to gen

Phil Wang 108 Nov 23, 2022
AlgoVision - A Framework for Differentiable Algorithms and Algorithmic Supervision

NeurIPS 2021 Paper "Learning with Algorithmic Supervision via Continuous Relaxations"

Felix Petersen 76 Jan 01, 2023
Predictive Modeling on Electronic Health Records(EHR) using Pytorch

Predictive Modeling on Electronic Health Records(EHR) using Pytorch Overview Although there are plenty of repos on vision and NLP models, there are ve

81 Jan 01, 2023
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 114 Jan 06, 2023
Variational autoencoder for anime face reconstruction

VAE animeface Variational autoencoder for anime face reconstruction Introduction This repository is an exploratory example to train a variational auto

Minzhe Zhang 2 Dec 11, 2021
TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

Fredrik Gustafsson 248 Dec 16, 2022
A machine learning malware analysis framework for Android apps.

🕵️ A machine learning malware analysis framework for Android apps. ☢️ DroidDetective is a Python tool for analysing Android applications (APKs) for p

James Stevenson 77 Dec 27, 2022
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017

Continual Learning Through Synaptic Intelligence This repository contains code to reproduce the key findings of our path integral approach to prevent

Ganguli Lab 82 Nov 03, 2022
Implementing DropPath/StochasticDepth in PyTorch

%load_ext memory_profiler Implementing Stochastic Depth/Drop Path In PyTorch DropPath is available on glasses my computer vision library! Introduction

Francesco Saverio Zuppichini 13 Jan 05, 2023
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020)

Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang UC

449 Dec 27, 2022
[ACM MM 2019 Oral] Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Contents Cycle-In-Cycle GANs Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Acknowledgments Relat

Hao Tang 67 Dec 14, 2022
Making Structure-from-Motion (COLMAP) more robust to symmetries and duplicated structures

SfM disambiguation with COLMAP About Structure-from-Motion generally fails when the scene exhibits symmetries and duplicated structures. In this repos

Computer Vision and Geometry Lab 193 Dec 26, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
A fast Evolution Strategy implementation in Python

Evostra: Evolution Strategy for Python Evolution Strategy (ES) is an optimization technique based on ideas of adaptation and evolution. You can learn

Mika 251 Dec 08, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023
Invariant Causal Prediction for Block MDPs

MISA Abstract Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challeng

Meta Research 41 Sep 17, 2022