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https://github.com/benitomartin/nlp-news-classification
NLP News Classification
https://github.com/benitomartin/nlp-news-classification
cnn exploratory-data-analysis jupyter-notebook multinomial-naive-bayes neural-networks python rnn streamlit
Last synced: 16 days ago
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NLP News Classification
- Host: GitHub
- URL: https://github.com/benitomartin/nlp-news-classification
- Owner: benitomartin
- Created: 2023-10-10T18:13:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-20T18:09:27.000Z (9 months ago)
- Last Synced: 2024-11-08T10:10:01.727Z (2 months ago)
- Topics: cnn, exploratory-data-analysis, jupyter-notebook, multinomial-naive-bayes, neural-networks, python, rnn, streamlit
- Language: Jupyter Notebook
- Homepage: https://nlp-news-classification.streamlit.app/
- Size: 16.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# NEWS CLASIFFICATION 🗞️
This repository hosts a notebook featuring an in-depth analysis of several **Neural Networks** models (RNN, CNN, feed-forward) and **Multinomial Naive Bayes** along with an app deployment using Streamlit. The following models were meticulously evaluated:
- Basic Multinomial Naive Bayes
- Basic Keras Model
- LSTM Model
- LSTM GRU Model
- LSTM Bidirectional Model
- TextVectorization + Keras Embedding
- Text_to_word_sequence + Word2Vec Embedding
- Basic CNN Model
The dataset used has been downloaded from [Kaggle](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset/data) and contains a set of Fake and Real News.
The app can be tested following this [link](https://nlp-news-classification.streamlit.app/). Feel free to ⭐ and clone this repo 😉
## 👨💻 **Tech Stack**
## 📐 Set Up
In the initial project phase, a set of essential helper functions was created to streamline data analysis and model evaluation. These functions include:
- **Plot Word Cloud**: Generates a word cloud for a specific label value and displays it in a subplot.
- **Plot Confusion Matrix**: Visualizes classification results using a confusion matrix.
- **Plot Precision/Recall Results**: Computes model accuracy, precision, recall, and F1-score for binary classification models, returning the results in a DataFrame.
## 👨🔬 Data Analysis
The first step of the project involved a comprehensive analysis of the dataset, including its columns and distribution. The dataset consists of two files (fake and true), each with the following columns:
- Title
- Text
- Subject
- Date
### Labels Distribution
Upon merging the datasets, it became apparent that the labels are well-balanced, with both fake and true labels at approximately 50%, negating the need for oversampling or undersampling. The dataset initially contained 23,481 fake and 21,417 true news articles, with 209 duplicate rows removed.
### Subjects Distribution
The subjects column revealed eight different topics, with true news and fake news being allocated in different subjects. This indicates a clear separation of labels within subjects.
### WordCloud
A word cloud visualization showed that the terms "Trump" and "US" were among the most common words in both label categories.
## 📶 Data Preprocessing
In parallel with data analysis, several preprocessing steps were undertaken to create a clean dataset for further modeling:
- Removal of duplicate rows
- Elimination of rows with empty cells
- Merging of the text and title columns into a single column
- Dataframe cleaning, including punctuation removal, elimination of numbers, special character removal, stopword removal, and lemmatization
These steps resulted in approximately 6,000 duplicated rows, which were subsequently removed, resulting in a final dataset of 38,835 rows while maintaining a balanced label distribution.
### Final Labels Distribution
## 👨🔬 Modeling
The project involved training several models with varying configurations, primarily consisting of five CNN models, one CNN model combined with Multinomial Naive Bayes.
### Model Results
### Model Performance Evaluation
All models demonstrated impressive performance, consistently achieving high accuracies, frequently surpassing the 90% mark. The model evaluation process involved several steps:
1. **Baseline Model with GridSearch:**
- A Multinomial Naive Bayes model was established using the TfidfVectorizer.
- Despite being a basic model, it set the initial benchmark for performance.
2. **Advanced Models with TextVectorization and Keras Embedding:**
- A series of models were tested with advanced text vectorization and embedding techniques.
- These models consistently reached accuracies exceeding 99%.
- The enhanced vectorization and embedding significantly improved model performance.
3. **Best-Performing Model: LSTM Bidirectional with Tokenization and Word Embedding:**
- The LSTM Bidirectional model, known for its sequence modeling capabilities, was identified as the best performer.
- It was further evaluated with a different tokenizer and embedding, specifically using `text_to_word_sequence` and Word2Vec embedding.
- While the performance remained impressive, it exhibited a slightly lower accuracy compared to the other models.
## 👏 App Deployment
The last step was to deploy an app using Gradio. The app can be tested following this [link](https://nlp-news-classification.streamlit.app/).
