/Emotion-Analysis-using-Deep-Learning

This project performs emotion analysis on a statement after cleaning the data set to analyse if the emotion attached with the tweet was sad, love etc using LSTM sequence classification model

Primary LanguageJupyter Notebook

Emotion-Analysis-using-Deep-Learning

Introduction about the project

The idea behind the project is to study the basic human emotions that can be depicted by a text written by a person. Data is generated at an unprecidented rate and more than a billion users are connected by means of social medium websites like Facebook, Twitter, Instagram, Whatsapp etc. Language is a medium that a human use to convey and share information with other people. With each message associated with a emotion behind it and to predict that emotion this Deep Learning NLP model is created.

One of the key factors influencing how people react to and behave during a crisis is their digital or non-digital social network, and the information they receive through this network. Publicly available online social media sites make it possible for crisis management organizations to use some of these experiences as input for their decision-making.

This project performs emotion analysis on different statements to analyse the emotions attached with a statement.

Basic human emotions classifications

  • Happiness
  • Sadness
  • Fear
  • Disgust
  • Anger
  • Surprise

Frameworks

  • Keras
  • Numpy
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn

Resources