Disaster Tweets Analysis and Classification

Project Description

Twitter has become an important communication channel in times of emergency.
The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time.
Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).

• Total approach towards the project can be seen on kaggle

  • Machine Learning approach : https://www.kaggle.com/mohitnirgulkar/disaster-tweets-classification-using-ml
  • Deep Learning approach : https://www.kaggle.com/mohitnirgulkar/disaster-tweets-classification-using-deep-learning

Project Contents

1. Exploratory Data Analysis
2. EDA after Data Cleaning
3. Data Preprocessing using NLP
4. Machine Learning models for classifying Tweets data
5. Deep Learning approach for classifying Tweets data
6. Model Deployment

Resources Used

• Packages : Pandas, Numpy, Matplotlib, Plotly, Word-cloud, Tensorflow, Scikit-Learn, Keras, Keras-tuner, Nltk etc.
• Dataset : https://www.kaggle.com/c/nlp-getting-started
• Word Embeddings : https://www.kaggle.com/danielwillgeorge/glove6b100dtxt

1. Exploratory Data Analysis

• Visualising Target Variable of the Dataset

  

• Visualising Length of Tweets

  

• Visualising Average word lengths of Tweets

  

• Visualising most common stop words in the text data

  

• Visualising most common punctuations in the text data

  

2. EDA after Data Cleaning

• We use Python Regex library and nltk lemmatizing methods for Data Cleaning