TY  - JOUR
T1  - Exploiting Noisy Data Normalization for Stock Market Prediction
AU - Mezhar, Assia AU - Ramdani, Mohammed AU - El Mzabi, Amal 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 1
SP  - 69
EP  - 77
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.69.77
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.69.77
KW  - Stock market
KW  -prediction
KW  -natural language processing
KW  -social media
KW  -data mining
KW  -normalization
AB  - Stock market prediction has grown to be an interesting and intriguing research area in the field of big
data analytics, predictive analytics and statistical analysis. The field of stock prediction has employed machine
learning and artificial intelligence techniques to forecast the behavior of the financial market and to predict stock
prices. Recently, social media has evolved to incorporate a massive amount and variety of textual data. The
analysis of this information furthers the mining of public sentiment and opinions about real-time trends. In
addition, the study of the inherently complex social media feeds promises new opportunities to discover
empirical regularities to measure economic activity and analyze economic behavior at high frequency and in
real-time. However, the noisy and short nature of social media feeds mask this information: unlike structured
news content, social media content is characterized by the presence of metadata related to social media sites,
(e.g., hashtags for Twitter) and the extensive usage of casual language, unstructured grammar, colloquial words,
ad hoc multi-token nonstandard lexical items such as acronyms and abbreviations that need situational context
to be interpreted and don’t fit with traditional technical analysis simply based on forecasting models. Under
those purposes and in order to meet the trading challenge in today’s global market, technical analysis must be
reconsidered. Before using any analysis model, data need to be preprocessed and regularities must be reviewed.
So, the precision of the forecasting and prediction systems of the financial market and stock prices will be
optimized and improved, also the accuracy of the data analysis models will be higher than state-of-art models.
In this context, this study introduces the challenges of the noisy information overload from social media, gives
a brief description of stock market prediction and its methodologies. Then, we discuss some of the current
methods of stock prediction methodologies and emphasis the need of new improved ones which are more
adapted to the context of noisy data. Finally, we present a new approach for the financial market forecasting
and prediction which uses data preprocessing and normalization from noisy data in Twitter. The strong
influence of the proposed data normalization model on the proposed approach’s precision and accuracy can
lead to a better results than traditional ones.
ER  -