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• W4221093365 abstract "Background: Investors estimate how a company's stock or financial instrument will perform in the future, which is known as the stock market prediction. Stock markets are one of the many industries that have benefited substantially from the incredible breakthroughs in machine learning. To effectively estimate these markets, many researchers and companies are continually researching and developing various state-of-the-art approaches and algorithms. Objective: The objective is to predict stock prices based on public sentiments. With a big collection of data from microblogging sites like Twitter, it is possible to analyse the thoughts or feelings of users on a wide scale. These sentiments play a major part in the way the stock market works. We review multiple papers and provide the advantages and disadvantages of various methods. Method: An in-depth examination of the most recent methodologies for predicting stock market values using sentiment analysis is offered, as well as the multiple consequences for stock markets when epidemics or major events occur. Results: According to the findings, impact sentiment analysis has a significant part in predicting stock market price movement, allowing for greater profit. Conclusion: With modern machine learning and deep learning processes, we can forecast stock costs with a few degrees of precision. This research examines how stock expectations have changed over time, as well as the most recent and effective technique for forecasting, supplying, and minimizing speculators' losses." @default.
• W4221093365 created "2022-04-03" @default.
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• W4221093365 date "2023-01-01" @default.
• W4221093365 modified "2023-09-26" @default.
• W4221093365 title "A Study on the Impact of Sentiment Analysis on Stock Market Prediction" @default.
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• W4221093365 doi "https://doi.org/10.2174/2666255815666220315153545" @default.
• W4221093365 hasPublicationYear "2023" @default.
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