The aim of this project is to build a movie recommendation system by filtering emotions using multi-label mood classification of the text input to the system by the user

• Text input to the system is used for Multi-label classification using RoBERTa which outputs the probabilities of all the 5 classes
• emotion2genre matrix has been created using data from a survey conducted
• Emotion vector is converted to vector of genres by matrix multiplication with emotion2genre matrix
• Movies are recommended using cosine similarity of the genre vector with other genre vectors in the movies dataframe
• The entire pipeline is deployed using Flask

• GoEmotions dataset by the Google research team has be used
  • All text with "Neutral" and "Disgust" emotion have been removed from the dataset
  • The emotions have been reduced to 5 from 28 using the ekmann mapping
  • The emotions have been one hot encoded to convert it into appropriate form for Multi-label Classification
• IMDB dataset has been used for the recommendation system.
  • Movies with 8+ IMDB rating are retained
  • Movies with Hindi and English languages are retained
  • Movies pertaining to India, USA and UK are retained

1. Create a Virtual Environment preferably with Anaconda

conda create -n redbot

2. Activate the virtual environment

conda activate redbot

2. Install the Requirements file

pip install -r requirements.txt

3. Change directory to the cloned directory

4. Run download_weights.py to download weights and move them to the appropriate location

python download_weights.py

5. Run the app.py file using the following command

python app.py

• Debarshi Chanda
• Manan Kakkar