Twitter - Fake News Classification Model

Machine learning model (based on Natural Language Processing) for classification of tweets into two categories: true information or fake news.
Dataset comes from Kaggle: Fake News Detection

End Goal

We will be building a solution based on NLP (Natural Language Processing).
Our task will be to create a model that can classify text into one of two groups:

• True information (label 1)
• Fake information (fake news, label 2)

Best Model Evaluation Summary

• Best Model: Support Vector Machine (SVM)
• Best Parameters: C = 10, gamma = 'scale', kernel = 'rbf'
• Key Performance Metrics:
  • Precision: 0.98
  • Recall: 0.97
  • F1-Score: 0.98
  • Accuracy: 98%
  • Weighted Average Precision: 0.98

The Support Vector Machine model achieved the highest precision (0.98) and was chosen as the best-performing model.

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Technologies Used:

• pandas: Data manipulation, cleaning, and saving datasets.
• ydata-profiling: Generating exploratory data analysis profiling reports.
• nltk: Stopword removal and text tokenization.
• spacy: Lemmatization and advanced text processing.
• re: Regular expressions for text cleaning.
• gensim: For training the Word2Vec model and word vector analysis.
• sklearn: For training models, hyperparameter tuning, and classification reports.
• numpy: For numerical data manipulation and vector operations.

Table of Contents

1. Notebook 1 - "1_data_preprocessing"
2. Notebook 2 - "2_vectors_report"
3. Notebook 3 - "3_model_training_and_tuning"

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Notebook 1 - "1_data_preprocessing"

1. Data Exploration & Profiling

• Steps:
  • Loaded the dataset from a CSV file.
  • Generated a profiling report for exploratory data analysis to understand the dataset distribution.
• Technologies Used:
  • pandas: For data manipulation and analysis.
  • ydata-profiling: For generating a profiling report of the dataset.

2. Data Filtering & Inspection

• Steps:
  • Filtered articles related to "politics" from the dataset for specific analysis.
  • Checked for missing values and duplicates in the dataset.
• Technologies Used:
  • pandas: For data filtering and inspecting missing or duplicate entries.

3. Data Cleaning

• Steps:
  • Dropped rows with missing values to ensure data consistency.
  • Combined the 'Headline' and 'Body' columns into a single feature for better context.
  • Removed unnecessary columns like 'URLs'.
• Technologies Used:
  • pandas: For data cleaning and feature engineering.

4. Text Preprocessing

• Steps:
  • Normalized text by converting it to lowercase and removing numbers and special characters.
  • Removed stopwords to reduce noise in the data.
  • Tokenized the text and performed lemmatization to standardize words.
• Technologies Used:
  • nltk: For stopword removal and text tokenization.
  • spacy: For lemmatization and advanced text processing.
  • re: For regular expressions to clean unwanted characters.

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Notebook 2 - "2_vectors_report"

1. Data Loading

• Steps:
  • Loaded the preprocessed dataset from a JSON file.
• Technologies Used:
  • pandas: For loading the dataset into a DataFrame.

2. Tokenization

• Steps:
  • Tokenized the 'Headline+Body' column by splitting the text into individual words.
• Technologies Used:
  • pandas: For applying tokenization on the dataset.

3. Training Word2Vec Model

• Steps:
  • Trained a Word2Vec model on the tokenized text with specified hyperparameters.
  • Retrieved word vectors for analysis and explored word similarities (e.g., "politics", "trump").
• Technologies Used:
  • gensim: For training the Word2Vec model and exploring word vectors.
  • numpy: For manipulating and calculating word embeddings.

4. Creating Article Vectors

• Steps:
  • Calculated the average word vector for each article by averaging the word embeddings of the tokens in the article.
  • Added the calculated average vectors as new columns in the dataset.
• Technologies Used:
  • numpy: For handling vector operations and calculating the average vectors for each article.

5. Train/Test Split

• Steps:
  • Split the dataset into training and testing sets using an 80/20 ratio.
• Technologies Used:
  • sklearn: For splitting the dataset into train and test sets.

6. Model Training & Evaluation

• Steps:
  • Trained multiple classification models: Random Forest, Logistic Regression, and Support Vector Machine (SVM).
  • Generated classification reports to evaluate the performance of each model.
• Technologies Used:
  • sklearn: For implementing and training classification models, as well as generating classification reports.

7. Saving Data

• Steps:
  • Saved the dataset with added article vectors to a new JSON file for future use.
• Technologies Used:
  • pandas: For saving the final dataset to a JSON file.

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Notebook 3 - "3_model_training_and_tuning"

1. Data Loading

• Steps:
  • Loaded the dataset containing article vectors from a JSON file.
• Technologies Used:
  • pandas: For loading the dataset into a DataFrame.

2. Model Training & Hyperparameter Tuning

• Steps:
  • Prepared the feature matrix X (article vectors) and target variable y (labels).
  • Split the dataset into training and testing sets using an 80/20 ratio.
  • Used GridSearchCV for hyperparameter optimization on three models:
    • Random Forest Classifier
    • Logistic Regression
    • Support Vector Machine (SVM)
• Technologies Used:
  • sklearn: For model training, hyperparameter tuning (GridSearchCV), and data splitting.

3. Model Evaluation

• Steps:
  • Evaluated each model using the classification report to obtain precision, recall, and F1-score.
  • Printed the classification report for each model, showing the best-performing hyperparameters.
• Technologies Used:
  • sklearn: For generating classification reports and evaluating model performance.

4. Best Model Selection

• Steps:
  • Compared the models based on their precision scores from the classification report.
  • Selected the best model based on the highest precision value.
• Technologies Used:
  • sklearn: For precision comparison and selecting the best-performing model.

5. Result Analysis

• Steps:
  • Printed the best model and its corresponding precision value for final analysis.
• Technologies Used:
  • sklearn: For result analysis and final model selection.