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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM2986

Stock Market Analysis Using Supervised Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM2986

Volume:

4 |

3 |

Mar 2020

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

HVN

Hasna V N

SPA

Shemitha P A

DGK

Dr. G Kiruthiga

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Abstract

The abstract aims to address the correlation between stock market movements and public sentiments expressed on Twitter. It delves into the utilization of sentiment analysis and supervised machine learning techniques to explore this connection. The study leverages Word2vec for textual representation, examining how shifts in stock prices align with sentiments expressed in tweets about specific companies. The investigation underscores the potential impact of positive news and social media sentiments on stock prices, emphasizing a demonstrated correlation between fluctuations in stock prices and sentiments conveyed in Twitter.-

KEYWORDS

Hashtag Collection Data Collection Real-Time Stock History Data Positive Keywords Negative Keywords Polarity Computation Sentiment Analysis Sentiment Index Computation Sentiment Discrepancy Index Price Prediction Yahoo Finance API.

Cite This Article

Hasna V N, Shemitha P A, Dr. G Kiruthiga, "Stock Market Analysis Using Supervised Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 4, no. 3, Mar 2020.

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Publication Date

1/14/2024

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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