ML- Enhancing Game Review Classification

ML-Powered Game Review classification

Game Review classification using Machine Learning (ML) involves processing textual data to categorize reviews as positive, negative, or neutral.
The first step is data preprocessing, which includes removing stopwords, tokenization, stemming/lemmatization, and converting text into numerical format using TF-IDF (Term Frequency-Inverse Document Frequency). TF-IDF helps in extracting important words while reducing the impact of common terms. After transformation, the dataset is split into training and testing sets, ensuring a balanced evaluation.

For classification, we can use Naïve Bayes, Logistic Regression, or deep learning models like LSTMs and BERT . Traditional ML models like Naïve Bayes are lightweight and effective for small datasets, while Transformer-based models (BERT, GPT) provide superior contextual understanding for large-scale reviews. The trained model is evaluated using metrics like accuracy, precision, recall, and F1-score to measure its performance. Finally, the model can be deployed via a web API to automate real-time review classification for businesses, improving decision-making and customer insights.