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Collecting and preprocessing stock-related information
The step of collecting and preprocessing stock-related information using text mining techniques is key to building a stock price prediction model. Text data can be collected from social media platforms such as Twitter, Facebook, and Instagram by scraping posts where users share their opinions on stocks, and news articles can be extracted from events or news that affect the stock market.
Categorize stock-related text through sentiment analysis
The next step is to categorize the collected text data into positive, negative, and neutral through sentiment analysis. Sentiment analysis plays an important role in extracting sentiment from texts to derive information that can influence the stock market. If there are many investors with positive opinions, the stock is likely to go up, and if there are many investors with negative opinions, the stock is likely to go down.
Build a stock price prediction model using machine learning algorithms
With the text data related to the stock classified through sentiment analysis, you can apply machine learning algorithms to build a stock price prediction model.
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