Zhyvr

Sentiment Analysis

The Kurdish Sentiment Analysis project focuses on analyzing the sentiment of text written in the Kurdish language. Sentiment analysis is a crucial technique used to determine the emotional tone behind a piece of text, categorizing it as positive, negative, or neutral. With millions of speakers across the Kurdistan region, spanning Iraq, Iran, Turkey, and Syria, Kurdish is a widely spoken language with unique cultural and linguistic characteristics.

This project was developed specifically during Hackasuly (Hackathon), aiming to address the need for sentiment analysis tailored to the Kurdish language, while offering solutions that could be adapted to any language.

Purpose

The primary goal of this project is to create a robust model capable of accurately classifying the sentiment of Kurdish text. The potential applications of such a model are vast and include:

1. Social Media Monitoring Understanding public opinion, trends, and user sentiment on social platforms in real-time.
2. Customer Feedback Analysis Businesses and organizations can analyze customer feedback, reviews, and comments to gauge satisfaction and improve services.
3. Political Opinion Tracking Tracking sentiment related to political events, elections, or public policies in the Kurdish-speaking regions.
4. Market Research Providing insights into public sentiment that can inform business decisions, advertising strategies, and product development.

By analyzing Kurdish text sentiment, individuals, organizations, and companies can gain valuable insights into public opinion, preferences, and sentiments, enabling better-informed decisions and targeted actions.

Methodology

1. Data Collection The project began by collecting a diverse dataset of Kurdish text from sources such as social media, forums, news articles, and customer feedback platforms.
2. Preprocessing The dataset was cleaned by removing irrelevant text, handling linguistic nuances (such as dialect variations), and standardizing Kurdish script.
3. Training Model A machine learning model, leveraging natural language processing (NLP) techniques, was trained on this preprocessed dataset. The model applied techniques such as word embeddings (like FastText) and supervised learning to classify sentiments.
4. Evaluation The performance of the model was evaluated based on metrics like accuracy, precision, recall, and F1-score. Cross-validation was employed to ensure robustness and minimize overfitting.

Dependencies

To develop and run this sentiment analysis model, the following dependencies are required:

• NLTK (Natural Language Toolkit) A comprehensive library for text processing and NLP tasks.
• scikit-learn Used for building and evaluating machine learning models.
• FastText Helps generate word embeddings that capture semantic meaning, crucial for sentiment analysis.
• pandas & NumPy Useful for data manipulation and preprocessing.

Conclusion

The Kurdish Sentiment Analysis project has immense potential for applications in social media monitoring, customer feedback, political analysis, and market research. By specifically focusing on the Kurdish language, this project contributes to addressing the gap in sentiment analysis for underrepresented languages. However, its methodologies and techniques can be adapted and applied to other languages, making it a versatile tool in the field of natural language processing.

The source code can be found on my GitHub repo