AI-Driven Market Trend Prediction, Business Strategy Advisor and Sentiment Analysis

Abstract

In today’s fast-paced business landscape, the ability to anticipate market trends and rapidly adapt strategies is paramount. This thesis introduces an innovative AI-driven framework that integrates market trend prediction, business strategy advising, and sentiment analysis to empower organizations with data-driven insights. By harnessing advanced machine learning techniques and natural language processing, the proposed system analyzes historical market data alongside real-time social media sentiment to forecast emerging trends. It emphasizes the importance of forecasting as a core function, combining traditional statistical approaches with AIenhanced techniques to better predict and understand market behavior. The framework employs state-of-the-art time series forecasting and machine learning methods to achieve high prediction accuracy, while its sentiment analysis component provides valuable context regarding consumer attitudes and market dynamics. Extensive experiments demonstrate that this integrated approach offers a robust decision support tool for strategic business planning. Our empirical evaluation revealed that the AutoGluon model outperformed traditional models such as ARIMA, Holt-Winters, and Prophet in forecasting accuracy, while VADER sentiment scores successfully captured fluctuations in market sentiment, contributing to more precise and contextualized predictions. This result underscores the effectiveness of combining quantitative trend modeling with qualitative sentiment analysis for enhanced business insight. Ultimately, this research contributes to the evolution of AI in business analytics, paving the way for more adaptive, informed, and timely decisionmaking in volatile market environments.