Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Dataset

The dataset consists of text data labeled with video domain categories (e.g., AI , Cyber security ,.. ). It is preprocessed and split into features (text) and labels (video categories) for training the classification models.

Approach

1. Data Preprocessing: Clean and preprocess the text data, including tokenization, removing stopwords, and vectorizing text using TF-IDF (Term Frequency-Inverse Document Frequency).

2. Feature Selection: Select relevant features using techniques such as chi-square test to identify the most informative features for classification.

3. Model Selection: Evaluate the performance of different classification algorithms, including Logistic Regression, Multinomial Naive Bayes, Random Forest Classifier, and Linear Support Vector Classifier (LinearSVC).

4. Model Training and Evaluation: Train each model on the training data and evaluate its performance using accuracy metrics on both training and testing datasets.

5. Selection of Final Model: Choose the model with the highest accuracy on the testing dataset as the final model for video segmentation.

Model Evaluation

The following classification algorithms were tested for sentiment analysis:

• Logistic Regression
• Multinomial Naive Bayes
• Random Forest Classifier
• Linear Support Vector Classifier (LinearSVC)

After evaluating the performance of each model, LinearSVC was selected as the final model due to its high accuracy on the testing dataset.

Usage

1. Data Splitting: Split the dataset into training and testing sets.

2. Model Training: Train each classification model using the training dataset.

3. Model Evaluation: Evaluate the performance of each model using accuracy metrics on both training and testing datasets.

4. Final Model Selection: Choose the model with the highest accuracy on the testing dataset as the final model for video classification.