Mendeley
TY  _  JOUR
ID  -  1403032647047
TI  -  Stock Trend Prediction Using Sentiment Index and Enhanced SVM with an Entropy-Based Sentiment Cost Function
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  yaghoubzadeh mahin
AU  -  Ebrahimi moghadam Abbas
AU  -  Khademi Morteza
AU  -  Sadoghi Yazdi Hadi
AD  -  گروه برق، دانشکده مهندسی، دانشگاه فردوسی مشهد
AD  -  دانشگاه فردوسی
AD  -  دانشگاه فردوسی مشهد
AD  -  Ferdowsi University of Mashhad
Y1  -  2025
PY  -  2025
VL  -  4
IS  -  
SP  -  278
EP  -  286
KW  -  Fin-BERT
KW  -   Sentiment Analysis
KW  -   Stock Market Prediction
KW  -   SVM
DO  -  
N2  -  <p style="direction: ltr;">Stock market prediction has always been a focus of researchers. Advances in artificial intelligence and machine learning algorithms have enabled the use of textual data alongside numerical data for better stock market forecasting and performance. In this research, to predict the trend of the New York Stock Exchange (NYSE) index, numerical data, textual data, and a machine learning model were employed. The model's input includes numerical data as well as the results of sentiment analysis from texts extracted from X (formerly Twitter). Sentiment analysis is performed using a specific machine learning algorithm, Fin-BERT. Additionally, to improve prediction results, prior knowledge of data distribution is incorporated into the cost function of the proposed classifier (SVM). This knowledge is obtained through the calculation of sentiment entropy. Experimental results show that incorporating sentiment entropy into the model's cost function improves prediction performance.</p>
UR  - rimag.ir/en/Article/47047
L1  - rimag.ir/en/Article/Download/47047
ER  -  
