Sentiment Analysis using Python

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Introduction

This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.

This repository holds the source code for the Minor project made for the 4th Semester of BE-CSE(IoT).

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Dependencies

• Python 3.8, OpenCV, Tensorflow
• To install the required packages, run (after cloning the repo)

pip install -r requirements.txt

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Basic Usage

The project is currently compatible with tensorflow-2.0 and makes use of the Keras API using the tensorflow.keras library.

• First, clone the repository and enter the folder

git clone https://github.com/TheFenrisLycaon/Sentiment-Analysis.git
cd Sentiment-Analysis

• The Dataset should palced inside a directory named Data.

• If you don't have the dataset already, Download the FER-2013 CSV from from here and place it under Data after unzipping.

• Then run ::

python src/dataGen.py

• To train this model, use:

python src/train.py

NOTE :: The model is already trained and the binary is placed inside the bin folder. Retraining the model will overwrite this binary. You may want to do this in case you want to get more/less accuracy by tweaking the epoch levels.

• If you want to view the predictions without training again, you can simply run:

python src/main.py

• To capture the frame at any point press SPACEBAR. The image will be saved in logs.
• Press ESC to exit.

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• The final folder structure is of the form:

|
|__ bin
|       |__ emotion.h5
|   
|__ Data
|       |__ test
|       |       |__ Emotion1  
|       |           |__ img_1.png 
|       |           |__ img_2.png
|       |           |__ img_3.png 
|       |           |...  
|       |       |__ Emotion_2  
|       |           |__ img_1.png
|       |           |__ img_2.png
|       |           |__ img_3.png
|       |           |...  
|       |       |...  
|       |__ train
|       |       |__ Emotion1  
|       |           |__ img_1.png
|       |           |__ img_2.png  
|       |           |__ img_3.png  
|       |           |...  
|       |       |__ Emotion_2  
|       |           |__ img_1.png
|       |           |__ img_2.png
|       |           |__ img_3.png
|       |           |...  
|       |           |... 
|       |__ fer2013.csv
|   
|__  src
|       |__ dataGen.py
|       |__ main.py
|       |__ train.py
|       |__ utils.py
|       |__ haarcascade_frontalface_default.xml.xml
|   
|__ requirements.txt

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• This implementation by default detects emotions on all faces in the webcam feed. With a simple 4-layer CNN, the test accuracy reached 63.2% in 50 epochs.

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Algorithm

• First, the .xml file is used to detect faces in each frame of the webcam feed.

• The region of image containing the face is resized to 48x48 and is passed as input to the CNN.

• The network outputs a list of softmax scores for the seven classes of emotions.

• The emotion with maximum score is displayed on the screen.

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Example

You can see the examples here