Covid-19-Tweets-Classification

Classification of Covid-19 Tweets using Multinomial Naive Bayes and TF-IDF Vectorizer to categorize tweets into three main classes: "Vaccine" (Vaksin), "Prevention and treatment of covid-19" (Pencegahan dan pengobatan), and "Current development of covid-19 in Indonesia" (Perkembangan covid di Indonesia).

About the Project

This project is a major assignment project for the first semester of the natural language processing course. The objective of this task is to crawl data related to COVID-19 on the Twitter platform with a minimum amount of data per class of 100, then classify the text based on the data obtained in these 3 classes.

The 3 classes used in this project are:

• "Vaccine" (Vaksin),
• "Prevention and treatment of covid-19" (Pencegahan dan pengobatan), and
• "Current development of covid-19 in Indonesia" (Perkembangan covid di Indonesia)

In this project, we use Multinomial Naive Bayes algorithm to perform text classification and TF-IDF Vectorizer as Word Embedding (to convert text data into vectors).

Objectives/ Problems

Covid-19 is a global virus pandemic affecting people worldwide. The virus spreads quickly through droplets from infected individuals. The Covid-19 pandemic has significantly affected societal life. Discussions range from health protocols and symptoms to daily reports of cases and vaccine development. Social and economic consequences are also widely discussed.

Twitter, a major social media platform, is a prominent space for discussions. Users share information through tweets, contributing to a vast array of discussions on COVID-19, like health protocols, symptoms, daily case reports, vaccine development, and socio-economic impacts. Due to the large volume of discussions on Twitter, a classification system is essential. This system helps analyze and understand the prevalent topics related to the Covid-19 pandemic.

Technology Used

• Python
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Sastrawi
• Tweepy

Notebook File

• covid-19-tweets-classification.ipynb

Workflow

Data Collection

• The dataset used in this project comprises tweet data acquired from the Twitter platform using the Tweepy library. The data was collected based on the following keywords:

  • "Vaccine" (Vaksin),
    • "vaksin astrazeneca"
    • "vaksin sinovac"
    • "vaksin sinopharm"
  • "Prevention and treatment of covid-19" (Pencegahan dan pengobatan), and
    • "pencegahan covid-19"
    • "pengobatan covid-19"
    • "pencegahan corona"
    • "pengobatan corona"
  • "Current development of covid-19 in Indonesia" (Perkembangan covid di Indonesia)
    • "perkembangan covid-19"
    • "covid-19 di Indonesia"
    • "covid-19 berkembang"

• The quantity of collected data is:

  • "Vaccine": 115 Tweets
  • "Prevention and treatment of covid-19": 155 Tweets
  • "Current development of covid-19 in Indonesia": 147 Tweets
  • Total: 417 tweets

• Data distribution:

  

Data Preprocessing

The data preprocessing steps applied to the data include:

• Remove hashtag, @user, and hyperlink from the tweet
• Stopword removal using Sastrawi library
• Train the TF-IDF Vectorizer model

Data Splitting

• The data is split into training data and testing data with a ratio of 0.2, signifying 80% for training data and 20% for testing data. The random_state variable is set to 0.

Model Building & Training

• The model is trained using the Multinomial Naive Bayes algorithm
• The trained model is saved to: models/MNB_model.sav

Model Evaluation

• Confusion Matrix:

  

• Classification Report

  	Precision	Recall	F1-Score	Support
  Pencegahan atau Pengobatan	0.90	0.93	0.91	28
  Perkembangan COVID-19	0.97	0.90	0.93	31
  Vaccine	0.92	0.96	0.94	25
  Accuracy			0.93	84
  Macro Avg	0.93	0.93	0.93	84
  Weighted Avg	0.93	0.93	0.93	84

• Accuracy Score:

  • 0.9285714285714286 (92.86%)

Publication

• Klasifikasi Tweet Mengenai COVID-19.pdf

Contributors

• Linggar Maretva Cendani - [email protected]

License

This project is licensed under the MIT License - see the LICENSE file for details