sohailanwarofficial/Darija-Sentiment-Analysis-with-DarijaBERT

This project performs sentiment analysis on Darija (Moroccan Arabic) text using the DarijaBERT model. It takes input from an Excel file containing tweets and an Excel file containing dialect information. The sentiment analysis is performed on each tweet, and the results are saved to a new Excel file. # Darija Sentiment Analysis with DarijaBERT This project performs sentiment analysis on Darija (Moroccan Arabic) text using the DarijaBERT model. It takes input from an Excel file containing tweets and an Excel file containing dialect information. The sentiment analysis is performed on each tweet, and the results are saved to a new Excel file. ## Overview The code provided in this repository includes: - Loading tweets and dialect information from Excel files. - Tokenizing the tweets for dialect analysis and sentiment analysis using the DarijaBERT tokenizer. - Running the DarijaBERT model for dialect analysis. - Running the DarijaBERT model for sentiment analysis. - Classifying the sentiment of each tweet (e.g., Positive or Negative). - Saving the sentiment analysis results to a new Excel file. ## Getting Started Before running the code, make sure to set up your environment and specify the paths to your Excel files containing tweets and dialect information: ```python # Usage example tweet_file = '/content/drive/MyDrive/Sentiment Analysis/ElecMorocco2016 (1).xlsx' dialect_file = '/content/drive/MyDrive/Sentiment Analysis/MOROCCODialect Stop-Words.xlsx' perform_sentiment_analysis(tweet_file, dialect_file) ``` Ensure that you have the required libraries installed: ```bash !pip install transformers ``` ## DarijaBERT Model The code loads the DarijaBERT tokenizer and model for both dialect and sentiment analysis. These models are designed for processing Darija (Moroccan Arabic) text. ## Sentiment Analysis The sentiment analysis process involves: 1. Tokenizing each tweet for dialect and sentiment analysis. 2. Passing the input through the DarijaBERT models for dialect and sentiment analysis. 3. Classifying the sentiment of each tweet, for example, as Positive or Negative. You can further customize the sentiment analysis process or extract additional information from the model outputs as needed. ## Saving Results The sentiment analysis results, including th …