The stock market has become increasingly unpredictable in recent years due to various factors like public sentiments, economy and geopolitical issues. The Traditional methods being used like time series model and Long Short-Term Memory (LSTM) models, often don’t make the correct predictions as they rely mostly on historical data of stock market and so they fail to grasp how market behaves or how chaotic behavior of market can be analyzed. These models hence may fail in case of making wise investment decisions. Our proposed methodology comes up with a hybrid approach using chaos theory, sentimental analysis for overcoming these challenges by analyzing the how stock prices might change according to the sentiments of people. We analyze 6,500 tweets of 95 organizations and their stocks and use chaos theory to find hidden patterns in stock movements. The classical computers take high computational time to analyze complex problems like stock market predictions. Hence, we combine these approaches with the Quantum Approximate Optimization Algorithm (QAOA) to solve the complex patterns of stock price prediction faster and more accurately than classical methods. We have used sentimental analysis, chaos theory with QAOA which is a combinatorial algorithm, being used to optimize the stock portfolio based on specific stock metrics–inclusive of F1 score (from sentimental analysis) and chaos theory assessments, it researches for the organizations with stability and low risk-high returns in stock market. Thus aiding investors and traders to make an informed decisions regarding where to invest with low risk and high returns.