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Sentimental Analysis Using Embedding Techniques

Lokesh Shrikhande, Sanmesh Yashwantrao, Shubham Vanage, Abhijeet More

Abstract


The purpose of this research is to assist the stock traders. Stock prices have been demonstrated to fluctuate based on how people perceive about a company. News reports are an excellent predictor of this. The software helps to automate the process by pulling news from Google and performing sentiment analysis on it. Traders can then use this data to choose whether to trade or hold their stocks. This project's purpose is to compare various sentiment analysis approaches. Sentiment analysis is carried out through using deep learning techniques. We want to assist day traders and short stock sellers who must be on edge in order to decide whether to buy or sell a stock depending on its performance, but cannot keep an eye on it for the entire duration. News about the company whose stock they own is one of the signs that helps them decide. However, if a trader receives negative news about a company in the morning and sees it in the evening, the stock value would have plunged by that time. So, every 20 min, we will run a python script that will scrape news websites for any news stories about the firm the trader is interested in, analyze the sentiment of those articles, and decide whether to buy more shares, sell them, or do nothing. Our suggested method will distinguish between favorable and negative evaluations, allowing users to determine whether to buy, hold, or sell stocks depending on the results.


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References


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DOI: https://doi.org/10.37628/ijocspl.v8i1.780

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