The Effectiveness of Adaptive Driven Data Dictionary on Inflectional Text Simplification for Financial Prediction

Stock prediction trend based on financial news is a challenging and interesting phenomenon in the data analysis research that expounded throughout soft-computing techniques. Remarkable machine-learning techniques expanded on binary and multinomial classification for the semantic word to define the prediction trend from financial news. The study of the research addressing the key component analysis with ML algorithm in where financial text simplified by different level of adjective on the same semantic categories. Textual adjective intensity influence on the stock market movement that defines as one the key component as discovered in this paper throughout developing text classification algorithms. Herein, two methodological approaches simultaneously perform on the same platform in terms of prediction and accurate classification from textual documents.