Yutong Yao¹ and Derek Lam², ¹USA, ²California State Polytechnic University, USA

FinanceVox, addresses the impact of digital media on the financial market by predicting stock prices through sentiment analysis [1]. The program comprises three interconnected components: Firebase for data storage, an AI backend for real-time insights, and Flutter for a user-friendly interface. Experiment A tests stock prediction accuracy, revealing a conservative AI but emphasizing the importance of refining algorithms and data quality. Experiment B assesses the scalability of the AI backend, indicating its effectiveness in handling increased user interactions. Methodology comparisons highlight FinanceVox's comprehensive approach compared to scholarly solutions, incorporating diverse data sources, NLP, and LSTM models [2]. Limitations include a single data source (Twitter) and the need for more diverse datasets. Improvements involve expanding data sources, enhancing data quality, and continuous algorithm updates for market adaptability [3]. Overall, FinanceVox aims to provide users with reliable stock predictions based on holistic sentiment analysis from various online platforms.

Artificial Intelligence, Stock Market Analyzing and Prediction, Social-Oriented Model, Machine Learning