ASPECT BASED SENTIMENT ANALYSIS ANDROID APP FOR FINANCIAL NEWS

Abstract

This research addresses the crucial need to adjust to the rapidly changing financial environments through a mobile application developed on Android that effectively incorporates innovative machine learning algorithms. Targeting the sentiment analysis of the financial news, the app applies aspect-based sentiment analysis, predictive modeling, and also predictive eligibility assessment for loans or credit cards. The aspect-based approach provides a deeper understanding, while the predictive modeling enables round-the-clock planning. The research helps in democratizing financial information and decision-making, making advanced analytics accessible to the user no matter their level of expertise in finance. Building on a user-friendly interface, the Android app arises as an innovator leading at the crossroads of technology and finance by giving many unique insights into financial decisions.

Keywords: Android Application, Machine Learning, Sentiment Analysis, Predictive Modeling, Aspect-Based Sentiment Analysis, Financial News, Predictive Eligibility Assessment, Democratization of Financial Information.