# Attention-based Bidirectional LSTM for Deceptive Opinion Spam Classification

arXiv: https://arxiv.org/abs/2112.14789 · HTML: https://mlsanity.com/paper/2112.14789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
- [Automated Crowdturfing Attacks and Defenses in Online Review Systems](https://mlsanity.com/paper/1708.08151/) (2017)
- [Hierarchical Attentional Hybrid Neural Networks for Document Classification](https://mlsanity.com/paper/1901.06610/) (2019)

## Built on by (most cited first, 1 of 1)

- [Text generation for dataset augmentation in security classification tasks](https://mlsanity.com/paper/2310.14429/) (2023)

## Abstract (arXiv)

Online Reviews play a vital role in e commerce for decision making. Much of the population makes the decision of which places, restaurant to visit, what to buy and from where to buy based on the reviews posted on the respective platforms. A fraudulent review or opinion spam is categorized as an untruthful or deceptive review. Positive reviews of a product or a restaurant helps attract customers and thereby lead to an increase in sales whereas negative reviews may hamper the progress of a restaurant or sales of a product and thereby lead to defamed reputation and loss. Fraudulent reviews are deliberately posted on various online review platforms to trick customers to buy, visit or distract against a product or a restaurant. They are also written to commend or discredit the product's repute. The work aims at detecting and classifying the reviews as deceptive or truthful. It involves use of various deep learning techniques for classifying the reviews and an overview of proposed approach involving Attention based Bidirectional LSTM to tackle issues related to semantic information in reviews and a comparative study over baseline machine learning techniques for review classification.