gandalf1819/Stock-Market-Sentiment-Analysis
Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
This project helps financial analysts and traders understand how news sentiment might influence stock prices. By analyzing news articles related to a company, it generates a sentiment score (positive or negative) and compares it against actual stock price movements. The output provides insights into potential correlations between market sentiment from news and stock performance, aiding in fundamental analysis.
142 stars. No commits in the last 6 months.
Use this if you are a financial analyst or trader looking to incorporate news sentiment as a factor in your stock market trend identification and fundamental analysis.
Not ideal if you are solely focused on technical analysis using historical price patterns without considering external news factors.
Stars
142
Forks
35
Language
R
License
GPL-2.0
Category
Last pushed
Nov 10, 2020
Commits (30d)
0
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