Abstract:

The “Zero-shot Multilingual Sentiment Analysis with Transformers” project addresses the challenge of sentiment analysis across multiple languages without language-specific training data. Leveraging state-of-the-art transformer models and Python-based web technologies, this project introduces a zero-shot approach to sentiment analysis, allowing the system to analyze sentiment in languages it has never seen during training.

Problem Statement:

Existing sentiment analysis models often require language-specific training data, making them less effective in languages with limited labeled datasets. The project aims to overcome this limitation by developing a zero-shot multilingual sentiment analysis system capable of accurately classifying sentiment in any given language without pre-training on that language.

Motivation:

With the increasing globalization of online content, there is a growing need for sentiment analysis tools that can understand and analyze sentiment in various languages. The motivation behind this project is to create a versatile and language-agnostic sentiment analysis system that can adapt to the diverse linguistic landscape of online communication.

Existing System:

The current sentiment analysis systems heavily rely on language-specific models. These models excel in languages for which they have been trained but struggle with languages lacking sufficient labeled data. The existing gap in multilingual sentiment analysis motivates the development of a more flexible and adaptable solution.

Proposed System:

The proposed system introduces a zero-shot learning approach, leveraging transformer-based models, such as BERT or GPT, for sentiment analysis. The system will be trained on a diverse set of languages but will be capable of accurately analyzing sentiment in any language without requiring specific training data for that language.

Modules Explanation:

1. Data Collection:

• Gather diverse multilingual datasets containing text samples for sentiment analysis.

1. Preprocessing:

• Prepare and preprocess the data to feed into the transformer model.

1. Transformer Model Training:

• Train the transformer model using a zero-shot learning approach, enabling it to generalize across multiple languages.

1. Web Interface:

• Develop a user-friendly web interface allowing users to input text in any language and receive sentiment analysis results.

System Requirements:

1. Hardware:

• Standard computing hardware capable of training and running transformer models.

1. Software:

• Python for development.
• Deep learning frameworks (TensorFlow or PyTorch).
• Web development frameworks (Django or Flask).

Algorithms:

1. Transformer Model (e.g., BERT or GPT):

• Utilize transformer architectures for zero-shot sentiment analysis.

Architecture:

The system adopts a client-server architecture. The client side involves the user interface for input and output, while the server side handles the transformer-based sentiment analysis.

Technologies Used:

1. Programming Languages:

• Python for backend development.
• HTML, CSS, JavaScript for frontend development.

1. Web Framework:

• Django or Flask for building the web application.

1. Deep Learning Framework:

• TensorFlow or PyTorch for implementing and training transformer models.

Web User Interface:

The web interface provides a simple yet powerful platform for users to input text in any language. The system processes the input through the trained transformer model and displays sentiment analysis results in real-time.

This project aims to contribute to the field of sentiment analysis by developing a versatile and language-agnostic system capable of analyzing sentiment across multiple languages without the need for language-specific training data.

UML DIAGRAMS

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Deployment Diagram

Flow chart Diagram