chaimaaELgotbi/Morocco-AIOX-Sentiment-Analysis

# Title: UmojaHack Morocco: Sentiment Analysis with Decision Tree Classifier ## Description: This repository tackles the UmojaHack Morocco challenge, aiming to build a robust model for sentiment analysis on Arabic text data. The project leverages a Decision Tree Classifier to classify text into positive, negative, or neutral sentiment, demonstrating expertise in data preprocessing, feature engineering, machine learning model selection, and code implementation. ## Key Features: * Data Preprocessing: Arabic text data will be cleaned and preprocessed to handle dialect variations, normalization, and potential noise. This might involve techniques like: 1. Removing punctuation and stop words 2. Handling diacritics (e.g., normalization or removal) 2. Text normalization (e.g., converting slang or informal language) 2. Addressing emojis and emoticons (e.g., converting to text or sentiment labels) * Feature Engineering: Relevant features will be extracted or created to enhance the model's ability to distinguish sentiment. This might involve: 1. N-grams (capturing sequences of words) 2. TF-IDF (term frequency-inverse document frequency) to weigh word importance 3. Sentiment lexicons (mapping words to sentiment scores) 3. Part-of-speech (POS) tagging or other linguistic features * Decision Tree Model Training: A Decision Tree Classifier will be trained on the preprocessed data to learn the patterns of sentiment expression in Arabic text. Hyperparameter tuning will be explored to optimize model performance (e.g., adjusting tree depth, minimum samples per split). * Evaluation Metrics: The model's accuracy, precision, recall, and F1-score will be calculated to assess its effectiveness on unseen data. Visualization techniques like confusion matrices or ROC curves might be used to illustrate model performance and decision boundaries. * Error Analysis: The model's performance on different sentiment categories and types of Arabic dialects will be analyzed to identify areas fo …