DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Ashish Salunkhe

Last update: Dec 17, 2022

Related tags

Deep Learning cnn embeddings lstm attention glove deep-learning-architectures fake-reviews

Overview

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures

Dataset: https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz
https://www.kaggle.com/rtatman/deceptive-opinion-spam-corpus
The data includes 1,569,264 samples from the Yelp Dataset Challenge 2015. This subset has 280,000 training samples and 19,000 test samples in each polarity.
**Also, if you happen to refer my work, a citation would do wonders for me. Thanks! **
The following implementations are done:

Bidirectional LSTM with GLoVE 50D word embeddings
LSTM with GLoVE 100D word embeddings
LSTM with GLoVE 300D word embeddings
CNN-LSTM with Doc2Vec and TF-IDF
Attention mechanism with GLoVe 100D word embeddings
Logistic Regression
Multinomial Naive Bayes
Support Vector Machine - Stochastic Gradient Descent (SGD)

The results obtained were as follows:

Sr. No.	Model Accuracy (%)	Precision Score	Recall Score	F1 Score
1	MultinomialNB	90.25	0.9325	0.8601
2	Stochastic Gradient Descent (SGD)	87.75	0.8913	0.8497
3	Logistic Regression	87.00	0.8691	0.8601
4	Support Vector Machine	56.25	0.525	0.9792
5	Gaussian Naive Bayes	63.5	0.6424	0.6169
6	K-Nearest Neighbour	57.5	0.8604	0.1840
7	Decision tree	68.5	0.6681	0.7412

Model	Training accuracy(%)	Testing accuracy(%)
Bidirectional LSTM + GLoVe(50D)	92.17	88.13
LSTM + GLoVe(100D)	99.18	85.75
CNN + LSTM + Doc2Vec +TF-IDF	96.23	92.19
CNN + Attention + GLoVe(100D)	99.00	90.25
BiLSTM + Attention + GLoVe(100D)	99.18	89.27
CNN + BiLSTM + Attention + GLoVe(100D)	99.75	81.25
LogisticRegression + TF-IDF	99.11	87.21

Future scope includes improvement in the attention layer to increase testing accuracy. BERT and XLNet can be implemented to improve the performance further.

You might also like...

Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)

Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) authors: Boris Knyazev, Michal Drozdzal, Graham Taylor, Adriana Romero-Soriano Overv

462 Jan 3, 2023

Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

0 Mar 30, 2022

NFT-Price-Prediction-CNN - Using visual feature extraction, prices of NFTs are predicted via CNN (Alexnet and Resnet) architectures.

5 Nov 3, 2022

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) We consider how a user of a web servi

20 Aug 21, 2022

Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

AceNAS This repo is the experiment code of AceNAS, and is not considered as an official release. We are working on integrating AceNAS as a built-in st

6 Sep 7, 2022

Learning Versatile Neural Architectures by Propagating Network Codes

Learning Versatile Neural Architectures by Propagating Network Codes Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang,

36 Dec 6, 2022

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene

1.4k Jan 8, 2023

PyTorch 1.0 inference in C++ on Windows10 platforms

Serving PyTorch Models in C++ on Windows10 platforms How to use Prepare Data examples/data/train/ - 0 - 1 . . . - n examples/data/test/

88 Oct 15, 2022

Human Action Controller - A human action controller running on different platforms.

Human Action Controller (HAC) Goal A human action controller running on different platforms. Fun Easy-to-use Accurate Anywhere Fun Examples Mouse Cont

27 Jul 20, 2022

DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Related tags

Overview

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures

You might also like...

Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)

Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

NFT-Price-Prediction-CNN - Using visual feature extraction, prices of NFTs are predicted via CNN (Alexnet and Resnet) architectures.

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

Learning Versatile Neural Architectures by Propagating Network Codes

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

PyTorch 1.0 inference in C++ on Windows10 platforms

Human Action Controller - A human action controller running on different platforms.

Owner

Ashish Salunkhe

Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❤️

Internship Assessment Task for BaggageAI.

Keras like implementation of Deep Learning architectures from scratch using numpy.

This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

Fake videos detection by tracing the source using video hashing retrieval.

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

Towards Fine-Grained Reasoning for Fake News Detection