@article{MAKHILLJEAS201813315516,
    title = {Boosting Decision Trees for Prediction of Market Trends},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {3},
    pages = {552-556},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.552.556},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.552.556},
    author = {Sharmishta and},
    keywords = {Decision trees,random decision trees,AdaBoost, social media data,phases,business analyst},
    abstract = {Usage of social sites like Facebook, Twitter is increasing rapidly. People are using these sites for
getting feedback about any product or service. Social data is the best data which business analyst can use for
getting analysis results. From social data the investor can predict in which products the users are more
interested in or what changes the users want in service. The social data analysis will definitely increase the sale
or profit gained by the investor. The use of machine learning algorithms for analysing market data will add more
knowledge into the knowledge of investor. In this study, we have proposed a method for analysing market data
collected from social sites. We have shown the behaviour of different machine learning algorithms against
market data. It is found that AdaBoost algorithm with one level decision trees perform best against market data.
We have used AdaBoost to boost the performance of decision trees and proved with results. Different phases
of social media data mining are also explained in detail.}
    }