Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10001130,
  title    = {Semantic Enhanced Social Media Sentiments for Stock Market Prediction},
  author    = {K. Nirmala Devi and  V. Murali Bhaskaran},
  country   = {India},
  institution={Kongu Engineering College},
  abstract  = {Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
},
    journal   = {International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering},  volume    = {9},
  number    = {2},
  year      = {2015},
  pages     = {678 - 682},
  ee        = {http://waset.org/publications/10001130},
  url       = {http://waset.org/Publications?p=98},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 98, 2015},
}