Sentiment And Emotion Detection on text

This project focuses on applying Natural Language Processing (NLP) techniques to analyze sentiment and emotions in Arabic text data. The goal is to automatically detect the underlying sentiment (positive, negative, neutral) and specific emotions expressed in Arabic-language content such as social media posts, reviews, or survey responses.

Key Components:

Arabic Dataset:

The analysis uses a labeled Arabic dataset containing text samples annotated for sentiment categories and emotion labels. This enables supervised machine learning training and evaluation.

AraBERT:

AraBERT is a pre-trained language model for Arabic based on the BERT architecture.

It is designed to understand Arabic morphology, syntax, and semantics.

In this project, AraBERT is fine-tuned for sentiment classification, predicting whether a text conveys positive, negative, or neutral sentiment.

MARBERT:

MARBERT (Multidialect Arabic BERT) is another transformer-based model pre-trained on a massive collection of Arabic social media data, covering multiple dialects.

It is especially effective for informal and dialect-rich Arabic text.

In this project, MARBERT is fine-tuned for emotion detection, identifying emotions such as joy, sadness, anger, surprise, or fear in the text.

Methodology Overview:

Data Preprocessing:

Text cleaning (removing punctuation, diacritics, normalization).

Tokenization using AraBERT and MARBERT tokenizers.

Label encoding.

Model Fine-tuning:

Using transfer learning, each model is fine-tuned on the prepared dataset.

Training involves optimizing cross-entropy loss to improve prediction accuracy.

Evaluation:

The models are evaluated on a held-out test set using metrics such as accuracy, F1-score, and confusion matrix.

Deployment (Optional):

Models can be integrated into an application or API to classify new Arabic text inputs in real time.

Outcomes:

Sentiment Analysis: Automatic classification of the polarity of Arabic text.

Emotion Analysis: Recognition of fine-grained emotional states expressed in Arabic language content.

Enhanced understanding of user opinions, attitudes, and feelings in Arabic social media and customer feedback.