Sentiment Analysis-Based stock Market Prediction Using‎Optimization Algorithm and Machine Learning

  • Authors

    • Dr. Phani Kumar Solleti Assistant Professor, Department of Computer Science & Engineering, KoneruLakshmaiah Educational Foundation, Vaddeswaram, India
    • Dr.Sateesh Nagavarapu Associate Professor, Department of CSE, Malla Reddy College of Engineering, Maisammagude, Hyderabad, Telangana, India‎
    • Dr.K. Kavita Associate. Professor ,Dept of Math ,BVRIT HYDERABAD College of Engineering for Women, Hyderabad ،Telangana, India
    • Dr. S. Selvakanmani Associate Professor, Department of Information Technol ogy, RMK Engineering College, RSM Nagar, Kavaraipettai, GummidipoondiTaluk, Tiruvallur District, Tamil Nadu, India
    • Dr.S. Mathumohan professor/CSE, Fatima Michael College of Engineering and Technology, Madurai, India
    • yerragudipadu subbarayudu Associate Professor, Department of computer science and engineering, KoneruLakshmaiah Education Foundation ،Bowrampet, Hyderabad, India
    https://doi.org/10.14419/tp5wwe40

    Received date: July 9, 2025

    Accepted date: August 22, 2025

    Published date: October 27, 2025

  • Stock Market Price Prediction; Time Series; Random Forest; Flamingo Search Algorithm; Sentiment Analysis; Feature Extraction; Data ‎Preprocessing‎.

  • Abstract

    Stock prediction is the crucial model in online applications to sell the products to the customers based on their requirements. The main ‎problem in stock prediction is the high error rate and pattern recognition. In this paper, proposed a Flamingo Search Algorithm with ‎Random Forest (FSA-RF) for improving the performance of SMP with sentiment analysis. Moreover, historical stock dataset and stock ‎news dataset are collected from the net source and trained in the system. The datasets are updated to the developed FSA-RF model. Initially ‎data preprocessing is employed for synchronising the text through converting it into lowercase characters. Then polarity-based detection is ‎processed using stock news data for classifying the positive and negative polarities. Hereafter, sentiment analysis is executed to classify the ‎negative and positive sentiments based on the news dataset polarities. Sentiment analysis phase is used for accurate classification of positive, ‎neutral and negative sentiments that useful for predicting stock prices. Both datasets are combined and feature extraction is performed to ‎extract the relevant features related to stock market prices. Update the FSO fitness in random forest classification layer which classify the ‎stock prices accurately in the output layer. The designed model is implemented in python tool and the gained outcomes are validated with ‎other prevailing models in terms of accuracy, sensitivity, speciality, precision, and mean square error‎.

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  • How to Cite

    Solleti, D. P. K., Nagavarapu, D. ., Kavita, D., Selvakanmani, D. S. ., Mathumohan, D., & subbarayudu, yerragudipadu . (2025). Sentiment Analysis-Based stock Market Prediction Using‎Optimization Algorithm and Machine Learning. International Journal of Basic and Applied Sciences, 14(6), 570-585. https://doi.org/10.14419/tp5wwe40