[EXE] Develop a sentiment analyzer

[JG-SSYT]

Learning Goals

[Learning goals, bulleted/numbered list is preferred]
[e.g. learn the concept and the use of train/validation/test dataset using scikit-learn ]

Exercise Statement

[Explain and describe what the exercise is]
[e.g. apply simple random-forest model to classify titanic survivability from titanic data ]

Prerequisites

[Prerequisites, in terms of concepts or other exercises in this repo]
[e.g. random-forest model, stochastic gradient descent, exercise #32]

Data source/summary:

[Provide a succinct summary of what the data is and where it is from]
[e.g. This involves covid19 fatality dataset from John Hopkin's website (links..) ]

(Optional) Suggest/Propose Solutions

[e.g. I have the solution using PyTorch, will be happy to create pull request to include the exercise statement/solution]
[e.g. I think chapter 3 of A. Geron's textbook works out the solution for this exercise]
[e.g. fast.ai's chapter 5 has the perfect solution for this]

(Optional) Further Links/Credits to Relevant Resources:

[e.g. This exercise and solution's proposal came from a lab session from DL2020]