Optimizing Stock Predictions With Bi-Directional LSTM and Levy Flight Fuzzy Social Spider Optimization (LFFSSO)

Stock Market Prediction (SMP) has developed into a significant area of research, especially in recent decades. Major novelty of the work is to develop an Evolutionary Bidirectional Long Short-Term Memory (EBi-LSTM) framework depends on investors' sentiment in tweets to Stock Market (SM). In addition, three feature selectors: the Chi-Square Test (CST), Analysis Of VAriance (ANOVA) technique and Mutual Information (MI) method are introduced for the selecting most important features. Levy Flight Fuzzy Social Spider Optimization (LFFSSO) algorithm is used for optimal tuning of parameters in the Bi-LSTM classifier. EBi-LSTM algorithm has been worked on datasets like Twitter, Stock, Weather, and Coronavirus disease (COVID-19). The proposed model extends the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, and robustly optimized Bidirectional Encoder Representations from Transformers Retraining Approach (RoBERTa) for sentiment analysis. Highest results of 88.26%, 90.43%, 89.33% and 92.63% for precision, recall, F1-score and accuracy has been attained by proposed system.