Natural Language Processing with Disaster Tweets Classification part 2.

Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes.

Introduction: Business Problem

Description: Now we have created a basic model. The goal now is to iterate on this model by making a good validation dataset. This is an issue of try and error since there is no general way of doing this. Thus we have no insure that we can perform a lot of tests without a long training time.

Evaluation: The evaluation metric for this competition is F1.

Methodology

The project will be executed by completing the following tasks:

1. Notebook Setup
2. Imports and EDA(Exploritory Data Analysis)
3. Training
4. Create a validation set
5. Initial model
6. Use validation set of Part 1
7. Improving model
8. Huggingface login
9. Final model for part 2 notebook
10. Saving and Sharing model
11. Use the model via pipeline

Link to Github Repository

Link to Blog post

Link to huggingface model