Multimodal AI Agent - News Summarization & Sentiment Analysis

Abstract

Multi-modal AI agents have become a significant area of interest due to their ability to process and integrate information from multiple data sources. This paper presents the design and implementation of a personalized news aggregator built using multi-modal AI techniques. The system collects news articles, generates summaries using language models, and performs sentiment analysis to deliver relevant and customized content to users. The agent leverages state-of-the-art AI models and frameworks, demonstrating how intelligent automation can be applied to enhance user experiences.

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Introduction

With the exponential growth of online information, users are often overwhelmed by the sheer volume of content available. Multi-modal AI agents, which can process text, images, audio, and other data types, offer a promising solution by intelligently filtering and summarizing information. This project focuses on building a multi-modal AI agent designed to aggregate news, summarize articles, and analyze sentiments, making it easier for users to consume personalized content.

The agent combines Natural Language Processing (NLP) techniques, sentiment analysis, and summarization models. By leveraging APIs and pre-trained models, the agent automates the process of fetching news articles and presenting concise, sentiment-driven summaries to users.

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Related Work

Several works have explored personalized news aggregators and multi-modal AI systems:

• Personalized News Aggregators: Traditional news aggregators rely on keyword-based filtering, but recent advancements incorporate machine learning to provide more accurate recommendations.

• AI Summarization Models: Pre-trained models like GPT, BERT, and T5 have shown remarkable performance in text summarization tasks.

• Sentiment Analysis: Sentiment analysis using models like VADER and transformers has been widely used in social media and review analysis. This project builds on these concepts by integrating them into a unified multi-modal AI agent.

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System Architecture

The architecture of the multi-modal AI agent consists of the following components:

1. Data Collection Module: Fetches news articles from online sources using web scraping or APIs.

2. Summarization Module: Generates concise summaries using a pre-trained language model.

3. Sentiment Analysis Module: Analyzes the sentiment of the articles to classify them as positive, negative, or neutral.

4. User Interface: Displays the curated news summaries and sentiment scores in an intuitive format.

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Results and Discussion

The multi-modal AI agent was tested on various topics, including technology, finance, and health. The results showed that the summarization model provided coherent and informative summaries, while the sentiment analysis accurately classified the sentiment of the articles.

Key metrics evaluated include:

• Summarization Accuracy: Measured by comparing generated summaries with human-written summaries.

• Sentiment Classification Accuracy: Validated using a benchmark dataset of labeled news articles.

• User Satisfaction: Feedback was collected from users, indicating high satisfaction with the relevance and presentation of the news content.

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Applications and Use Cases

Multi-modal AI agents like the one presented in this project have several applications:

1. Personalized News Aggregators: Providing users with customized news feeds.

2. Market Sentiment Analysis: Analyzing financial news to gauge market sentiment.

3. Content Curation: Assisting content creators by summarizing and categorizing articles.

4. Customer Support: Enhancing automated customer support systems by integrating multi-modal information.

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Conclusion

This project demonstrates the potential of multi-modal AI agents in automating information retrieval and personalization. By integrating multiple AI models and techniques, the agent provides a seamless and efficient way to consume personalized news content. The results highlight the feasibility and effectiveness of such systems in real-world applications.

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Future Work

Future improvements to the project could include:

1. Incorporating Additional Modalities: Extending the agent to process images, audio, and video.

2. Improving Summarization Models: Using fine-tuned models for specific domains.

3. Enhanced User Interface: Developing a web or mobile application for a better user experience.

4. Multi-Language Support: Enabling the agent to process and summarize news in multiple languages.

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References

1. OpenAI API Documentation: https://platform.openai.com/docs

2. VADER Sentiment Analysis Tool: https://github.com/cjhutto/vaderSentiment

3. Hugging Face Transformers Library: https://huggingface.co/transformers

4. News API Documentation: https://newsapi.org

5. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Devlin et al., 2018.