Deception-Detection

Classifiers added to detect deception through transcripts. <br />Dataset Used:

1. Real life trial data collected during a series of experiments at Michigan (http://web.eecs.umich.edu/~zmohamed/PDFs/Trial.ICMI.pdf) (Folder - dataset)
2. Deceptive Opinion Spam Corpus v1.4 (https://myleott.com/op-spam.html)

Installation

Run the following command to install all python packages that'll be used in the project:

pip install -r requirements.txt

Approaches

Opinion Spam Dataset:

<b><u>Folder: OpinionSpam</u></b>

1. Parameters: NGram Approach Classifiers: SVM, NB, Random Forest

   To execute:
      python3 Ngrams_And_Classifiers.py 

SVM <br/> <br/> <br/> NB <br/> <br/> <br/> Random Forest <br/> <br/> <br/>

2. Parameters: LIWC Classifiers: SVM, NB, Random Forest

   To execute:
      python3 LIWC_And_Classifiers.py 

NB <br/> <br/> <br/> SVM <br/> <br/> <br/> Random Forest <br/> <br/>

<br/>

3. Parameters: NGrams, LIWC Classifiers: SVM

   To execute:
      python3 SVM_Ngrams_LIWC.py

<br/> <br/>

4. Recurrent Neural Networks

   To execute:
      python3 RNN

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Real Life Dataset

<b><u>Folder: RealLife</u></b>

1. Classifiers: SVM, NB

   To execute:
      python3  Classifiers.py

SVM <br/> <br/> <br/> NB <br/> <br/> <br/>

2. Parameters: NGram Approach Classifiers: SVM, NB, Random Forest

   To execute:
      python3 Ngrams_And_Classifiers.py 

SVM <br/> <br/> <br/> NB <br/> <br/> <br/> Random Forest <br/> <br/> <br/>

3. Parameters: LIWC Classifiers: SVM, NB, Random Forest

   To execute:
      python3 LIWC_And_Classifiers.py 

NB <br/> <br/> <br/> SVM <br/> <br/> <br/> Random Forest <br/> <br/>

<br/>

4. RNN

   To execute:
      python3 RNN.py

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