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https://github.com/linggarm/covid-19-tweets-classification

Classification of Covid-19 Tweets using Multinomial Naive Bayes and TF-IDF Vectorizer to categorize tweets about Covid-19 into three main classes
https://github.com/linggarm/covid-19-tweets-classification

artificial-intelligence classification covid-19 data-science google-colab machine-learning multinomial-naive-bayes naive-bayes natural-language-processing nlp python sastrawi scikit-learn text-classification tf-idf tweepy tweets tweets-classification twitter vaccine

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Classification of Covid-19 Tweets using Multinomial Naive Bayes and TF-IDF Vectorizer to categorize tweets about Covid-19 into three main classes

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# 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](notebooks/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:**



    ![images/data_distribution.png](images/data_distribution.png)



### 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](models/MNB_model.sav)



### Model Evaluation

  - Confusion Matrix:



    ![images/conf_mat.png](images/conf_mat.png)



  - 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](docs/PBA%20Tugas%20Besar%20-%20Kelompok%202.pdf)



## Contributors

* [Linggar Maretva Cendani](https://github.com/LinggarM) - [[email protected]](mailto:[email protected])



## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details