| --- |
| license: mit |
| language: |
| - en |
| - id |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - legal |
| --- |
| |
| # Election Tweets Classification Model |
|
|
| This repository contains a fine-tuned of ***indolem/indobertweet-base-uncased model*** for classifying tweets related to election topics. The model has been trained to categorize tweets into eight distinct classes, providing valuable insights into public opinion and discourse during election periods. |
|
|
| ## Classes |
|
|
| The model classifies tweets into the following categories: |
| 1. **Politik** (2972 samples) |
| 2. **Sosial Budaya** (425 samples) |
| 3. **Ideologi** (343 samples) |
| 4. **Pertahanan dan Keamanan** (331 samples) |
| 5. **Ekonomi** (310 samples) |
| 6. **Sumber Daya Alam** (157 samples) |
| 7. **Demografi** (61 samples) |
| 8. **Geografi** (20 samples) |
|
|
| | Encoded | Label | |
| |:---------:|:---------------------------:| |
| | 0 | Demografi | |
| | 1 | Ekonomi | |
| | 2 | Geografi | |
| | 3 | Ideologi | |
| | 4 | Pertahanan dan Keamanan | |
| | 5 | Politik | |
| | 6 | Sosial Budaya | |
| | 7 | Sumber Daya Alam | |
|
|
| ## Libraries Used |
|
|
| The following libraries were used for data processing, model training, and evaluation: |
|
|
| - Data processing: `numpy`, `pandas`, `re`, `string`, `random` |
| - Visualization: `matplotlib.pyplot`, `seaborn`, `tqdm`, `plotly.graph_objs`, `plotly.express`, `plotly.figure_factory` |
| - Word cloud generation: `PIL`, `wordcloud` |
| - NLP: `nltk`, `nlp_id`, `Sastrawi`, `tweet-preprocessor` |
| - Machine Learning: `tensorflow`, `keras`, `sklearn`, `transformers`, `torch` |
|
|
| ## Data Preparation |
|
|
| ### Data Split |
| The dataset was split into training, validation, and test sets with the following proportions: |
|
|
| - **Training Set**: 85% (3925 samples) |
| - **Validation Set**: 10% (463 samples) |
| - **Test Set**: 5% (231 samples) |
|
|
| ### Training Details |
| - **Epochs**: 3 |
| - **Batch Size**: 32 |
|
|
| ### Training Results |
|
|
| | Epoch | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | |
| |-------|------------|----------------|-----------------|---------------------| |
| | 1 | 0.9382 | 0.7167 | 0.7518 | 0.7671 | |
| | 2 | 0.5741 | 0.8229 | 0.7081 | 0.7931 | |
| | 3 | 0.3541 | 0.8958 | 0.7473 | 0.7953 | |
|
|
| ## Model Architecture |
|
|
| The model is built using the TensorFlow and Keras libraries and employs the following architecture: |
|
|
| - **Embedding Layer**: Converts input tokens into dense vectors of fixed size. |
| - **LSTM Layers**: Bidirectional LSTM layers capture dependencies in the text data. |
| - **Dense Layers**: Fully connected layers for classification. |
| - **Dropout Layers**: Prevent overfitting by randomly dropping units during training. |
| - **Batch Normalization**: Normalizes activations of the previous layer. |
|
|
| ## Usage |
|
|
| ### Installation |
|
|
| To use the model, ensure you have the required libraries installed. You can install them using pip: |
|
|
| ```bash |
| pip install transformers |
| ``` |
|
|
| ```python |
| # Load model directly |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| tokenizer = AutoTokenizer.from_pretrained("Rendika/tweets-election-classification") |
| model = AutoModelForSequenceClassification.from_pretrained("Rendika/tweets-election-classification") |
| ``` |
|
|
| ```python |
| # Use a pipeline as a high-level helper |
| from transformers import pipeline |
| |
| pipe = pipeline("text-classification", model="Rendika/tweets-election-classification") |
| ``` |
|
|
| ### Data Cleaning |
|
|
| The data was cleaned using the following steps: |
| 1. Converted text to lowercase. |
| 2. Removed 'RT'. |
| 3. Removed links. |
| 4. Removed patterns like '[RE ...]'. |
| 5. Removed patterns like '@ ... ='. |
| 6. Removed non-ASCII characters (including emojis). |
| 7. Removed punctuation (excluding '#'). |
| 8. Removed excessive whitespace. |
|
|
| ### Sample Code |
|
|
| Here's a sample code snippet to load and use the model: |
|
|
| ```python |
| import tensorflow as tf |
| from tensorflow.keras.models import load_model |
| import pandas as pd |
| |
| # Load the trained model |
| model = load_model('path_to_your_model.h5') |
| |
| # Preprocess new data |
| def preprocess_text(text): |
| # Include your text preprocessing steps here |
| pass |
| |
| # Example usage |
| new_tweets = pd.Series(["Your new tweet text here"]) |
| preprocessed_tweets = new_tweets.apply(preprocess_text) |
| # Tokenize and pad sequences as done during training |
| # ... |
| |
| # Predict the class |
| predictions = model.predict(preprocessed_tweets) |
| predicted_classes = predictions.argmax(axis=-1) |
| ``` |
|
|
| ## Evaluation |
|
|
| The model was evaluated using the following metrics: |
| - **Precision**: Measure of accuracy of the positive predictions. |
| - **Recall**: Measure of the ability to find all relevant instances. |
| - **F1 Score**: Harmonic mean of precision and recall. |
| - **Accuracy**: Overall accuracy of the model. |
| - **Balanced Accuracy**: Accuracy adjusted for class imbalance. |
|
|
| ## Conclusion |
|
|
| This fine-tuned model provides a robust tool for classifying election-related tweets into distinct categories. It can be used to analyze public sentiment and trends during election periods, aiding in better understanding and decision-making. |
|
|
| ## License |
|
|
| This project is licensed under the MIT License. |
|
|
| ## Contact |
|
|
| For any questions or feedback, please contact [me] at [rendikarendi96@gmail.com]. |
