Y R Mukund
ABSTRACT
The Phenomenon of stock markets is a complex one and is something which, has attracted researchers and statisticians for a long time. Complex statistics have long dominated this field where the prediction models are usually stochastic. The advent of machine learning gave us a new way of looking at the problem. Much work has been done in analyzing the stock market to predict the stock index of a particular organization. However, most of the work done is based on the previous stock data and other statistical parameters. Our work, uses data such as the online news articles about a particular company and aims to help a trader conclude the market sentiment towards that company through sentiment analysis. The online raw data is obtained through crawling and is indexed, weighted and subject to sentiment analysis to output the final sentiment of the market. It is found that the Naive-Bayesian Classifier is the more suitable option among the Decision Tree and Random Forests for the task of sentiment analysis. The Final Sentiment Factor arrived at, is found to reflect the real time market sentiment quite accurately. It is also shown that the sentiment factor can be used as an input to a more complex analysis model. This new model, performs better than the existing models.
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