A Study of Stock Market Prediction Through Sentiment Analysis
DOI:
https://doi.org/10.12723/mjs.64.6Keywords:
Bombay Stock Exchange (BSS), Logistic Regression, KNN-LR Hybrid Classifier, Naïve Bayes Algorithm, Particle Swarm Optimization (PSO), Sentiment Analysis, Stock Market, Support Vector Machine, Social Media, TwitterAbstract
In the modern world, the current state and course of economic development and growth are determined by the fortunes and vagaries of the stock markets. In this research study, the authors provide a model that can aid in making reliable and error-free predictions of stock market trends. The described approach uses sentiment analytics based on financial news and past stock market patterns. The proposed structure has been used to forecast stock market patterns that incorporates sentiment analysis taken from news and previous stock market patterns to provide more precise results. The model shown here has provided a two-step process- the Naive Bayes algorithm and forecasting future values of stocks using evaluation findings on text polarity and historical stock value movement information. A novel idea known as the KNN-LR Hybrid algorithm has been introduced to achieve better outcomes when evaluating the accuracy and efficacy of other machine learning algorithms.
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