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Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models

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Short Video-Analytics

Introduction

1. This small project is conducted to analyze the candidate's answer during video interview.
2. Project used existing pre-trained models
        * For video/image emotion analysis : FER library is used which has pre-trained model based on Harcascade and MTCNN
        * For speech to Text analysis :Speech_recognition library is used
        * For text sentiment analysis :VADER from ntlk library is used.VADER(Valence Aware Dictionary and sentiment Reasoner)
3. The Analysis is presented in the form of Heat-Maps showing neagtive-positive sentiments from words and negative and positive emotions of face in video.

Emotions-Sentiment Heat Maps

The sample of result/outcome of the code.

How to run this code

   1. Select directory ($ cd <directory>)
   2. Clone the repo  ($ git clone <repo-url>)
   3. Install dependencies  
      pip install -r requirements.txt 
            OR individual modules using 
      pip install <module_name>
   4. Place mp4 video file of small size 20-40sec introductory video in your working directory
   5. Change file_name in settings.py file
   6. Run the code "Video_Analytics.py"

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Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models

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